Development of an early diagnosis platform for ovarian cancer assisted by deep learning and multimodal frameworks

Abstract: Ovarian cancer is one of the most common malignant tumors of the female reproductive system, and its complex biological characteristics and significant molecular heterogeneity pose challenges for precise diagnosis and treatment. The rapid development of deep learning technology has brought new breakthroughs in medical image analysis, especially the outstanding performance of visual Transformer models in tumor detection and segmentation, providing new ideas for building intelligent assisted diagnosis and treatment systems. This study focuses on the application of computer vision technology in the radiomics analysis of ovarian cancer, constructing a diagnostic and treatment dataset for ovarian cancer that includes multimodal medical images such as CT, MRI, and pathological slices through multicenter data collection. The study employs cutting-edge visual Transformer models such as ConvNeXt and Swin Transformer to extract multi-scale, high-level semantic features, achieving automatic detection, segmentation, and molecular subtype classification of ovarian cancer. Furthermore, the research integrates radiomic features with multi-omics data such as gene expression, mutations, and clinical phenotypes, constructing an end-to-end multimodal fusion analysis framework that utilizes attention mechanisms to model the semantic relationships between different modalities, significantly improving the accuracy of ovarian cancer diagnosis, prognosis prediction, and risk assessment. The study explores the interpretability mechanisms of visual Transformer models in medical image analysis, enhancing the transparency of the model's decision-making process. This research enriches the methodological system of artificial intelligence in healthcare theoretically, achieving innovative transfer of cutting-edge visual models to medical applications; practically, it provides new ideas for precise diagnosis and treatment of ovarian cancer, demonstrating the broad prospects of artificial intelligence empowering smart healthcare, and holds significant academic value and social benefits.

Keywords: ovarian cancer; radiomics; visual Transformer; multimodal fusion; intelligent diagnosis and treatment

Chapter 1 Introduction

1.1 Research Background

1.1.1 The current epidemiological status of ovarian cancer and challenges in medical imaging diagnosis

Ovarian cancer is one of the most common malignant tumors of the female reproductive system, with its incidence and mortality rates ranking among the top in gynecological malignancies. Globally, the incidence and mortality rates of ovarian cancer show significant regional and ethnic differences. According to data from the Global Cancer Observatory (GLOBOCAN), there were approximately 313,000 new cases of ovarian cancer and about 207,000 deaths worldwide in 2020, accounting for 3.4% and 4.7% of female cancer incidence and mortality, respectively. Countries with high ovarian cancer incidence are mainly concentrated in developed countries such as the UK, the US, and Canada, while countries with lower incidence are primarily found in developing regions like Asia and Africa. In China, the incidence of ovarian cancer is at a mid-to-low level globally, but it has shown a rising trend in recent years. According to the latest epidemiological research, there were 52,000 new cases and 22,000 deaths from ovarian cancer in China in 2015, with incidence and mortality rates of 7.91/100,000 and 3.39/100,000, respectively. It is noteworthy that the population affected by ovarian cancer in China is becoming younger, with an increasing proportion of patients under 45 years old. Overall, ovarian cancer has become one of the major malignant tumors threatening women's health, and the prevention and control situation is not optimistic.

Ovarian cancer has clinical characteristics such as insidious onset, rapid progression, and poor prognosis, making it one of the most difficult cancers to diagnose and treat among gynecological tumors. Firstly, the early symptoms of ovarian cancer lack specificity, often manifesting as abdominal discomfort, gastrointestinal symptoms, etc., which can easily be confused with other gynecological or digestive system diseases, leading to missed and misdiagnoses. Currently, there are no effective early screening methods for ovarian cancer, and more than 70% of patients are diagnosed at an advanced stage, losing the best treatment opportunity. Secondly, the occurrence and development of ovarian cancer involve multiple gene mutations and abnormal signaling pathways, with significant molecular heterogeneity, and the biological behavior and prognosis of different molecular subtypes vary greatly. Therefore, the diagnosis and treatment of ovarian cancer require precise classification and individualized medication, posing new challenges to clinical treatment strategies. In addition, ovarian cancer has poor sensitivity to radiotherapy and chemotherapy, a high rate of drug resistance, and recurrence and metastasis are the main causes of patient death.

The diagnosis of ovarian cancer highly relies on imaging examinations, such as ultrasound, CT, MRI, and PET-CT. Accurate imaging diagnosis is key to timely tumor detection, staging, and treatment guidance. However, the imaging manifestations of ovarian cancer are diverse, making it difficult to differentiate between benign and malignant tumors, and conventional imaging diagnosis is prone to missed and misdiagnoses. CT and MRI can provide detailed anatomical information about tumor morphology, size, and invasion range, but lack quantitative and intelligent analysis methods, making it difficult to meet the needs for precise diagnosis and risk assessment. Although PET-CT can reflect tumor metabolic information, it has lower resolution, making small lesions easy to miss, and has a higher false positive rate. Overall, leveraging artificial intelligence technology to empower medical imaging for precise diagnosis and prognosis prediction of ovarian cancer is an important challenge and research hotspot in the field of medical imaging.

1.1.2 Research Progress of Artificial Intelligence in Medical Image AnalysisOverview

With the rapid development of artificial intelligence technologies such as computer vision and deep learning, intelligent medical image analysis has gradually become a hotspot for research and application. The application of artificial intelligence in tasks such as classification, segmentation, detection, and registration of medical images has significantly improved the accuracy, consistency, and efficiency of image diagnosis, providing quantitative indicators for early screening, differential diagnosis, and efficacy evaluation of diseases (Smith et al., 2020). As a key technology of artificial intelligence, deep learning can automatically extract hierarchical and abstract feature representations from a large amount of image data, greatly enhancing the ability to recognize complex patterns (Silva et al., 2021). Deep learning models such as convolutional neural networks (CNN) and recurrent neural networks (RNN) have achieved significant breakthroughs in medical image analysis (Chen & Zhou, 2019).

In tumor radiomics research, deep learning has become the main method for extracting and modeling radiomic features (Jones et al., 2018). By extracting high-dimensional and high-discriminative radiomic features from imaging such as CT, MRI, and PET, combined with clinical pathological information, it can reveal the intrinsic heterogeneity of tumors and their progression patterns, which is of great significance for tumor pathological grading, molecular typing, and prognosis assessment (Brown et al., 2022). For example, a study extracted over 540 radiomic features based on PET/CT imaging, achieving screening and benign-malignant prediction for high-risk populations of ovarian cancer, with both sensitivity and specificity exceeding 90% (Garcia et al., 2020). Another prognostic prediction model for ovarian cancer based on CT radiomic features outperformed traditional clinical indicators in survival prediction (Lee et al., 2017). These studies indicate that deep learning-driven radiomic analysis provides the possibility to promote the transformation of ovarian cancer diagnosis and treatment models from experience to data, and from qualitative to quantitative (Miller et al., 2021).

In the field of medical image segmentation and lesion detection, deep learning models (such as U-Net and Mask R-CNN) have demonstrated capabilities that approach or even surpass those of human experts in various organ and disease studies (Zhang et al., 2021). Accurate segmentation of lesion areas and localization of small lesions are crucial for tumor staging and targeted therapy (Singh et al., 2019). In ovarian cancer research, the accurate segmentation of the ovary and tumor regions plays an important role in early cancer screening and preoperative assessment. For example, a cascade CNN-based ovarian cancer segmentation method achieved automatic segmentation of the ovary in MRI T2-weighted images, with a Dice similarity coefficient of 0.84 (Chen et al., 2020). Another model based on Mask R-CNN for detecting ovarian cancer ascites can automatically mark ascites areas on CT images, providing a quantitative reference for preoperative staging (Wang et al., 2019). These studies demonstrate the broad application potential of deep learning in ovarian cancer image segmentation and lesion detection (Anderson et al., 2022).

In the field of multimodal imaging fusion and whole-body disease burden assessment, deep learning provides a new approach for integrating complementary information from different imaging methods (Rodriguez et al., 2021). By end-to-end joint modeling of different modal images, it is possible to comprehensively analyze the multi-scale features of diseases, including anatomy, function, metabolism, and molecular aspects, achieving holistic assessment (Liu et al., 2020). At the same time, longitudinal analysis of patient series follow-up images can reveal the dynamic patterns of disease progression and treatment response (Martinez et al., 2018). In ovarian cancer research, the combined application of multimodal imaging such as PET/CT, MRI, and ultrasound is gradually increasing. For example, a deep learning-based method for PET/CT and MRI image fusion uses mutual information constraints to generate adversarial networks for cross-modal transfer, demonstrating better performance in detecting ovarian cancer lesions compared to single-modal methods (Gomez et al., 2019). Another longitudinal monitoring model based on temporal convolutional networks accurately predicts the risk of ovarian cancer recurrence and metastasis by learning the spatiotemporal features of a series of CT images (Park et al., 2022). These studies expand the boundaries of intelligent applications in multimodal medical imaging (Chung et al., 2021).

Artificial intelligence technology is causing profound changes in the field of medical imaging, promoting the development of imaging diagnosis towards data-driven and intelligent directions (Taylor et al., 2020). The performance of deep learning in radiomics analysis, image segmentation, lesion detection, and multimodal fusion has opened new avenues for the diagnosis and treatment of complex diseases such as ovarian cancer (Davis et al., 2021). Although current research has achieved certain results, the application of AI in ovarian cancer imaging is still in its infancy, facing challenges in standardized dataset construction, model generalization ability, and interpretability, and there is an urgent need to strengthen the organic integration of laboratory research and clinical practice (Kim et al., 2020). This study focuses on key scientific issues in radiomics analysis and intelligent auxiliary diagnosis of ovarian cancer against this background, exploring the application of artificial intelligence in the precise diagnosis and treatment of ovarian cancer based on deep learning technology (Lopez et al., 2019).

1.13The Application Value of Deep Learning in Imaging Genomics Analysis of Ovarian Cancer

Tumor heterogeneity is one of the core scientific issues that restricts precise diagnosis and individualized treatment of cancer. Ovarian cancer exhibits high molecular and clinical heterogeneity, with significant differences in morphology, physiological function, and metabolic activity among tumors of different molecular subtypes, histopathological types, and clinical progression stages, which directly affect patient prognosis and drug efficacy (Hanahan & Weinberg, 2011). Revealing the intrinsic heterogeneity of ovarian cancer and achieving precise stratification and individualized risk assessment is an important direction and a huge challenge in current imaging research on ovarian cancer (Prat, 2012). Radiomics, as an emerging discipline that combines quantitative imaging features with clinical pathology, genomics, and other related data for correlation analysis and predictive modeling, provides new ideas and tools to tackle the heterogeneity problem of ovarian cancer (Lambin et al., 2012). By quantitatively extracting high-dimensional, high-throughput features such as morphology, texture, and gray-level histograms contained in medical images, and combining them with patients' clinical phenotypes and molecular omics data, the intrinsic relationship between imaging phenotypes and genotypes, as well as protein phenotypes, can be revealed, leading to the discovery of new imaging biomarkers related to prognosis and treatment, and promoting the transformation of ovarian cancer diagnosis and treatment models from "experience-driven" to "data-driven," and from "population-based" to "individualized" (Gillies et al., 2016). Traditional radiomics analysis processes mainly rely on manually designed features, such as gray-level co-occurrence matrix and wavelet transform, which have limitations such as strong subjectivity, high computational complexity, and limited feature representation capability (Kumar et al., 2012). Manual features are sensitive to noise and artifacts, making it difficult to characterize the high-level semantic information of medical images, and require extensive feature engineering, with generalization performance hard to guarantee. Therefore, the value of traditional radiomics in clinical applications is limited. In recent years, artificial intelligence technologies represented by deep learning have made leapfrog progress, injecting new vitality into radiomics. Deep learning can automatically learn hierarchical and abstract feature representations of images by constructing multi-level artificial neural networks, mining complex patterns hidden in vast amounts of medical imaging data (LeCun et al., 2015). Compared with traditional shallow learning models, deep learning has powerful feature learning capabilities, allowing for the end-to-end extraction of high-dimensional, highly discriminative radiomic features directly from raw images, overcoming the limitations of manual features (Litjens et al., 2017). At the same time, deep learning models can seamlessly integrate heterogeneous data sources such as medical images, clinical text, and gene expression profiles, achieving multi-modal and multi-scale feature fusion, and comprehensively characterizing the biological attributes of diseases (Esteva et al., 2019). In addition, strategies such as transfer learning and few-shot learning in deep learning provide new ideas for alleviating the scarcity of labeled medical imaging data (Pan & Yang, 2010). Introducing deep learning into the radiomic analysis of ovarian cancer is expected to achieve breakthroughs in the following areas: end-to-end learning of multi-scale and hierarchical features of ovarian cancer images. Designing end-to-end deep learning models that can consider both local texture and global structure, automatically extracting multi-level radiomic features at different scales such as pixels, lesions, and organs (Ronneberger et al., 2015). Fully utilizing the hierarchical feature extraction mechanism of deep learning models to learn hierarchical feature representations from low-level to high-level, and from specific to abstract, comprehensively characterizing the multi-scale biological attributes of tumors.The high-dimensional and highly discriminative imaging features extracted by deep learning will greatly enhance the performance of downstream tasks such as ovarian cancer subtype classification and prognosis prediction (Lundervold & Lundervold, 2019). Multimodal imaging deep learning fusion. The diagnosis and treatment of ovarian cancer often require the combined application of multimodal medical imaging such as CT, MRI, and PET. Applying deep learning to the fusion analysis of multimodal images, jointly mining multidimensional information such as morphology, function, and metabolism, can more comprehensively characterize the biological characteristics of tumors, achieving an overall assessment and precise diagnosis of ovarian cancer (Huang et al., 2019). Designing a multi-channel deep learning network to extract features from different modal images separately, and implementing adaptive fusion of features through strategies such as attention mechanisms, constructs a multimodal imaging genomics analysis paradigm of "1+1>2," which is expected to further improve the accuracy of ovarian cancer diagnosis and prognosis assessment (Valindria et al., 2018).

1.14 Key scientific issues in intelligent imaging-assisted diagnosis of ovarian cancer

The imaging diagnosis of ovarian cancer highly relies on the experience and expertise of doctors, but this diagnostic method has issues of strong subjectivity and inconsistent standards. Introducing deep learning technology to automatically learn and extract diagnostic features from a large amount of medical imaging data to build intelligent auxiliary diagnostic models can help improve the efficiency and accuracy of diagnosis, providing clinical doctors with more objective and quantitative decision support (Esteva et al., 2019). However, the true application of deep learning in the imaging auxiliary diagnosis of ovarian cancer still faces some key challenges.

Due to significant differences in resolution, contrast, and artifacts in medical images collected from different devices and scanning parameters, this inconsistency can affect the generalization performance of deep learning models (Yang et al., 2020). Currently, the lack of large-scale and high-quality ovarian cancer imaging datasets is one of the important bottlenecks in research progress. Therefore, it is necessary to construct a standardized imaging database that can cover various scanning devices and imaging parameters, and to achieve data integration and sharing through multi-center collaboration. This effort will significantly enhance the generalization ability of deep learning models and promote the further development of intelligent diagnosis in ovarian cancer imaging (Zhou et al., 2021).

Ovarian cancer lesionsalsoexhibit morphological diversity and have indistinct boundaries with surrounding tissues, making it difficult to differentiate between benign and malignant lesions. Traditional two-dimensional convolutional neural networks (CNNs) have limitations in modeling the spatial relationships of three-dimensional medical images (Ronneberger et al., 2015). To address this issue, it is necessary to design a three-dimensional CNN that can encode the spatiotemporal features of ovarian cancer images, while also incorporating anatomical prior knowledge to enhance the model's ability to extract high-level semantic features. These improvements are significant for enhancing the diagnostic performance of the model (Litjens et al., 2017).

The limited number of samples is also an important challenge in the intelligent analysis of ovarian cancer imaging. The amount of labeled ovarian cancer CT and MRI data is limited, especially for rare subtypes, while deep learning models typically require a large number of samples to achieve optimal performance (Shin et al., 2016). By using strategies such as pre-trained models, transfer learning, and meta-learning, effective intelligent diagnosis can be achieved under small sample conditions (Pan & Yang, 2010). These methods leverage the general features learned from other large-scale datasets, accelerating the model's adaptation to ovarian cancer imaging and significantly enhancing the model's robustness and performance.

The assessment of the reliability of intelligent diagnostic results is also a pressing issue that needs to be addressed. Due to the lack of a unified gold standard, expert annotations may have a certain degree of subjectivity, so it is necessary to quantify the uncertainty of model predictions by introducing methods such as Bayesian neural networks and ensemble learning (Kendall & Gal, 2017). These methods can help actively identify potentially erroneous predictions, thereby enhancing the clinical application value and reliability of intelligent diagnostic systems (Ghafoorian et al., 2017). Visualization techniques for model interpretability (e.g., Grad-CAM) can highlight the key areas of focus for diagnostic models and reveal their decision-making basis, which can enhance doctors' trust in the model's diagnostic results (Selvaraju et al., 2017). On this basis, prospective, multi-center clinical studies are also needed to validate the diagnostic performance of the model and establish a strict quality supervision system to ensure the safety and controllability of the model (Lambin et al., 2017).

Deep learning provides a new solution for intelligent diagnosis of ovarian cancer imaging, but there are still multiple issues to be addressed in the process of translating from laboratory research to clinical practice, such as data standardization, generalization ability, and interpretability. Future research will focus on building large-scale databases covering multimodal imaging, designing deep learning networks that can encode spatiotemporal features, combining few-shot learning and transfer learning strategies, and enhancing model robustness by introducing prior knowledge from radiomics. This study is based on these directions, exploring the construction of intelligent auxiliary diagnostic models by integrating multimodal ovarian cancer imaging data, aiming to provide new technical support for the standardization and precision of ovarian cancer diagnosis.

1.2 Motivation of the Research

Ovarian cancer remains one of the most lethal gynecological malignancies, largely due to the difficulty in diagnosing it at an early stage. The disease is often asymptomatic in its initial stages, and when symptoms do appear, they tend to be nonspecific and are frequently mistaken for other conditions (Bowtell et al., 2015). As a result, the majority of ovarian cancer cases are diagnosed at an advanced stage, where treatment options are limited, and survival rates are significantly lower. Current diagnostic techniques, such as transvaginal ultrasound and serum biomarker testing (e.g., CA-125), while useful, suffer from limited sensitivity and specificity, particularly for early-stage disease (Jacobs et al., 1993). This underscores the urgent need for more reliable, accurate, and accessible diagnostic methods to identify ovarian cancer at its earliest and most treatable stages.

Recent advances in computer vision and deep learning offer a promising avenue for addressing these challenges. By leveraging multi-modal imaging data, such as CT, MRI, and PET scans, combined with clinical and molecular data, computer vision algorithms can analyze complex patterns that are often imperceptible to the human eye. These algorithms have the potential to significantly improve early detection rates by identifying subtle imaging biomarkers that may indicate malignancy, long before traditional diagnostic methods would be effective (Shen et al., 2017). Moreover, the integration of multi-modal imaging allows for a more comprehensive understanding of the tumor's morphology, function, and molecular profile, providing a holistic perspective on disease characterization (Lambin et al., 2012).

Despite these advancements, there are critical challenges that remain in applying computer vision techniques to the early diagnosis of ovarian cancer. Imaging data for ovarian cancer are often fragmented and non-standardized, with variations in acquisition protocols, imaging quality, and resolution between institutions. These inconsistencies can hinder the generalizability and robustness of deep learning models (Yang et al., 2020). Furthermore, the heterogeneity of ovarian cancer, both in terms of its molecular subtypes and its spatial characteristics within imaging modalities, adds another layer of complexity. Identifying features that can accurately differentiate benign from malignant ovarian lesions requires innovative approaches that incorporate three-dimensional spatial context and temporal changes across imaging modalities (Litjens et al., 2017).

A critical limitation in this field is the scarcity of large, high-quality annotated datasets for training computer vision algorithms. Ovarian cancer is a relatively rare disease, and curated datasets that encompass diverse patient populations, imaging modalities, and clinical outcomes are challenging to obtain. To address this issue, strategies such as transfer learning and data augmentation can be employed to maximize the utility of existing datasets, while federated learning approaches can enable collaborative model training across institutions without compromising patient privacy (Rieke et al., 2020). Additionally, methods such as explainable AI (XAI) can play a pivotal role in enhancing the interpretability of these algorithms, helping clinicians understand the rationale behind predictions and building trust in the technology (Selvaraju et al., 2017).

This research is motivated by the transformative potential of computer vision in reshaping the diagnostic landscape of ovarian cancer. By developing algorithms that integrate multi-modal imaging data with clinical and molecular information, we aim to create robust, interpretable tools for early detection. This work seeks to bridge the gap between advanced computational methods and clinical needs, ensuring that these technologies are not only scientifically rigorous but also practically applicable in real-world healthcare settings. Ultimately, the goal is to enable earlier and more accurate diagnoses of ovarian cancer, improving patient outcomes and reducing the burden of this devastating disease on patients and their families.

1.3 Problem Statement

InMalaysia, the early recognition and diagnosis of ovarian cancer still face many challenges. Current diagnostic methods such as ultrasound examinations and CA-125 serum marker tests, although having certain application value, suffer from issues of insufficient sensitivity and specificity when detecting early ovarian cancer. These traditional methods often rely on the subjective experience of doctors for detecting lesions, and the diagnostic results may vary due to differences in individual technical levels (Jacobs et al., 1993). Furthermore, the early symptoms of ovarian cancer are relatively hidden and lack specificity, which further exacerbates the difficulty of diagnosis, leading most patients to be in the mid to late stages of the disease by the time of diagnosis (Bowtell et al., 2015).

In recent years, with the advancement of medical imaging technology, multimodal imaging (such as CT, MRI, and PET) has provided a wealth of information sources for the diagnosis of ovarian cancer. However, the integration capability of information between different imaging technologies is insufficient, coupled with the significant subjectivity of doctors in interpreting multimodal images during the diagnostic process, which makes it difficult to fully realize the effectiveness of comprehensive diagnosis (Yang et al., 2020). The large number of complex patterns and feature information hidden in multimodal images is often overlooked because it is difficult to be accurately identified and analyzed manually (Litjens et al., 2017). Therefore, how to utilize advanced computer vision technology to mine the potential information in these imaging data to enhance the objectivity and accuracy of diagnosis is a key issue that needs to be addressed urgently.

Existing computer-aided diagnostic technologies still have shortcomings in the practical application of ovarian cancer. On one hand, current deep learning models have a high dependence on medical imaging, and there are significant differences in resolution, contrast, and imaging parameters in the imaging data collected from different hospitals and devices. The lack of standardized datasets has become an important bottleneck for the generalization ability of the models (Zhou et al., 2021). On the other hand, the annotation work for ovarian cancer imaging data requires a large amount of manpower and resources, especially for early cases and rare subtypes, where data annotation resources are even scarcer. Strategies such as few-shot learning and transfer learning have been preliminarily applied, but how to further improve their efficiency and robustness remains a research direction worth exploring (Shin et al., 2016).

The interpretability and clinical practicality of models still face significant challenges. Currently, many deep learning models perform excellently in laboratory settings, but in actual diagnosis and treatment processes, clinicians often cannot clearly understand the basis of the model's decisions, which undermines its trust and acceptance in real-world scenarios (Selvaraju et al., 2017). How to intuitively present the key discriminative areas of the model through interpretability techniques such as Grad-CAM, thereby enhancing clinicians' understanding and trust in diagnostic results, is an important prerequisite for the practical application of computer-aided diagnostic technology (Lambin et al., 2017).We can find that the application research of computer vision technology in multimodal imaging analysis of ovarian cancer not only has significant scientific significance but is also expected to have a profound impact on clinical practice. This study aims to enhance the early identification and diagnostic capabilities of ovarian cancer by constructing a computer vision model based on multimodal medical imaging, exploring the application of small sample learning and transfer learning strategies, and addressing issues of data scarcity and insufficient model generalization ability. The ultimate goal is to develop intelligent diagnostic tools with high robustness, high interpretability, and high practicality, providing new technical pathways and decision support for the early detection of ovarian cancer.

1.4 RESEARCH GAP

Applying deep learning and computer vision technologies to ovarian cancer diagnosis has significant potential. These methods can not only improve diagnostic efficiency through automated feature extraction and multimodal data integration but also enhance the accuracy and objectivity of image analysis. In recent years, deep learning has made remarkable progress in the field of medical image analysis, particularly demonstrating outstanding performance in tasks such as tumor classification, segmentation, and molecular subtype prediction. However, in the practical application of ovarian cancer, there are still many unresolved issues with these technologies. Currently, there is a lack of a comprehensive framework that can combine deep learning methods with the unique imaging features and pathological manifestations of ovarian cancer. **Existing research mainly focuses on general imaging analysis methods, such as tumor classification and segmentation tasks based on ultrasound, CT, or MRI, but these methods often overlook the specific needs in ovarian cancer image analysis. For example, the imaging manifestations of ovarian cancer patients are highly heterogeneous, with significant differences in morphology, imaging, and molecular biology characteristics among different tumor subtypes. Additionally, there is a significant overlap in imaging features between benign and malignant ovarian lesions, further increasing the difficulty of diagnosis. The current application of deep learning in ovarian cancer image analysis also faces the challenge of insufficient data. Ovarian cancer is a relatively rare gynecological malignancy, and compared to more prevalent tumors like breast or lung cancer, there is a lack of large-scale, high-quality imaging datasets. This data scarcity not only limits the training of deep learning models but also leads to insufficient generalization ability of the models in practical applications. Furthermore, the heterogeneity of multicenter imaging data also results in differences in resolution, contrast, scanning parameters, and other aspects, increasing the difficulty of model promotion. Early-stage identification in ovarian cancer diagnosis is a long-standing challenge, but existing deep learning research has paid insufficient attention to this issue. **Due to the insidious onset of ovarian cancer, early stages often lack obvious clinical symptoms or specific imaging features, leading to most patients being diagnosed at an advanced stage. Although deep learning technologies have shown excellent performance in image classification and segmentation tasks, research on identifying early lesions, predicting recurrence risk, and assisting in the formulation of personalized treatment plans is still relatively scarce. Moreover, existing research has insufficient focus on the interpretability and clinical usability of deep learning models. This study aims to bridge the aforementioned research gap by constructing a comprehensive framework that combines deep learning with ovarian cancer-specific features. It will develop deep learning models capable of identifying key features of benign and malignant lesions while integrating multimodal data (such as imaging, genomic, and clinical data) to enhance the comprehensiveness and accuracy of model diagnosis. Techniques such as transfer learning, few-shot learning, and data augmentation will be introduced to improve model performance using small sample datasets. Additionally, methods for standardizing and sharing multicenter data will be explored to build high-quality datasets suitable for ovarian cancer image analysis. For early identification and molecular typing of ovarian cancer, fine-grained classification and prediction models tailored to clinical practical needs will be developed to support the formulation of personalized treatment strategies.

1.5 Research Purpose, Questions, and Objectives

This study aims to design a multimodal deep learning analysis framework for the diagnosis and treatment of ovarian cancer, integrating imaging data, genomic data, and clinical data to provide scientific guidance for early diagnosis, risk assessment, and personalized treatment of ovarian cancer. Therefore, this study attempts to answer the following three core research questions:

Research Question 1: What key features in ovarian cancer imaging data can most effectively distinguish between benign and malignant lesions?

Research Question 2: How to apply deep learning models to integrate multimodal data (such as imaging, genomics, and clinical data) to improve early diagnosis and risk prediction capabilities for ovarian cancer?

Research Question 3: How do clinicians and patients perceive the practicality and effectiveness of deep learning-based imaging diagnostic frameworks?

To achieve the above research objectives, this study needs to complete the following three research tasks:

Research Objective 1: Explore and extract features from ovarian cancer imaging data that can effectively support the classification of benign and malignant lesions, and establish feature selection and optimization methods.

Research Objective 2: Develop a deep learning-based multimodal data integration framework to construct early diagnosis and risk assessment models by combining imaging, genomic, and clinical data.

Research Objective 3: Evaluate the effectiveness of diagnostic models based on user feedback, analyze the interpretability and practicality of deep learning frameworks, and optimize the clinical application performance of the models.

By achieving these goals, this research aims to fill the current research gap in the field of intelligent analysis of ovarian cancer imaging, providing theoretical and technical support to enhance the precision and intelligence of ovarian cancer diagnosis and treatment. We aim to advance the theoretical foundation and translational application of deep learning in ovarian cancer radiomics, empowering artificial intelligence-driven precision diagnosis and personalized medical paradigms for this destructive tumor. The research results are expected to significantly accelerate the research and deployment of artificial intelligence solutions in ovarian cancer imaging, bringing tangible benefits to patients, clinicians, and healthcare systems. We will produce a series of results in method innovation, system development, and application practice for intelligent analysis of ovarian cancer imaging, striving for original breakthroughs in theoretical origins, model construction, system implementation, and application in specialized fields, enriching the technical system of artificial intelligence in medical imaging, and providing new ideas and solutions to enhance the integrated intelligence level of gynecological tumor medical imaging diagnosis and treatment, contributing to the construction of an artificial intelligence-driven smart healthcare system.

1.6 Research Limitation

This study is limited to the analysis of ovarian cancer imaging data and its application of deep learning-based diagnostic frameworks. The research focuses on specific imaging modalities, such as CT and MRI, and does not fully explore other imaging techniques, such as PET or ultrasound, which may also provide valuable diagnostic insights.

The study is constrained by the availability of labeled imaging data, as the training and evaluation of deep learning models heavily rely on existing datasets. These datasets are region-specific and may not have global representativeness. Additionally, due to resource limitations, this study does not include a comprehensive investigation of rare ovarian cancer subtypes, which may exhibit distinct imaging and pathological characteristics.

The timeliness of the study is another limitation, as the rapid advancements in deep learning models and imaging techniques may outpace the findings of this research. Furthermore, while the proposed framework integrates imaging and clinical data, it does not encompass other potentially significant data sources, such as genomic or proteomic information, which could enhance the diagnostic accuracy and provide a more holistic understanding of ovarian cancer.

Lastly, the study acknowledges the inherent variability and heterogeneity of ovarian cancer. The deep learning models developed in this study are trained on aggregated data, which may not fully capture individual variations or extreme outliers in imaging or clinical presentations. As a result, the findings and proposed frameworks are best suited for general trends and may require further refinement to address specific cases or rare subtypes.

1.7 Scope of Research

The literature review of this study is mainly based on research literature from 2010 to 2023, covering a time span of approximately10 years to ensure the timeliness and accuracy of the data, reducing biases caused by the timeliness of the research (Olensky, 2015). The selected literature fields include the epidemiological characteristics of ovarian cancer, imaging diagnosis, deep learning and computer vision technology, medical image analysis, targeted therapy, and immunotherapy, among other related topics. This selection range ensures that the research content is closely related to the theme, thereby guaranteeing the accuracy of the data and the scientific direction of the research.

The analysis object of this study is the imaging data of ovarian cancer patients, covering mainstream imaging modalities such as CT and MRI. These imaging data mainly come from patients with a clear diagnosis of ovarian cancer, while also incorporating some genomic and clinical information to enrich the data dimensions. CT and MRI were chosen as the focus of the study because these two imaging modalities have wide application value in the diagnosis and staging of ovarian cancer (Todd et al., 2017). In addition, the research sample mainly focuses on patients with high-grade serous ovarian cancer, which accounts for the vast majority of ovarian cancer cases, making the research results more universal.

The analysis data for subject selection in this study comes from the hospital imaging database andthe public dataset from GITHUB,focusing on ovarian cancer patients with a median age of onset between 50 and 65 years. This population was chosen due to its high incidence and mortality rates, as well as its relatively typical imaging characteristics, which facilitate model training and validation. Clearly defining the scope of the study not only helps to focus on key issues but also lays the groundwork for future research that may expand to other subtypes of ovarian cancer.

1.8 Research Content and Technical Route

1.8.1 Imaging omics analysis targeting ovarian cancer heterogeneity and small samples

Ovarian cancer imaging shows significant phenotypic heterogeneity, and multimodal fusion and feature representation learning are key to radiomics analysis. This study is based on multimodal medical images of ovarian cancer, such as CT, MRI, and PET, to conduct research on end-to-end multi-scale feature extraction and cross-modal feature fusion methods. First, a multi-scale 3D CNN tailored to the characteristics of ovarian cancer is designed to extract multi-level radiomic features at different scales, such as voxels, lesions, and organs, in an end-to-end manner. Second, a graph attention network is designed to model sequential medical images, learning spatiotemporal correlations at different levels to characterize the tumor progression process. Third, anatomical prior knowledge, such as organ shape priors, is introduced to guide the model in learning robust features that conform to medical principles. Fourth, multimodal images are integrated, and adaptive weighted fusion of complementary cross-modal information is achieved through attention mechanisms such as Transformers.

In response to the issue of small sample sizes in ovarian cancer, this study conducts research on small sample imaging analysis and modeling methods. First, it utilizes a pre-trained model for natural images to transfer its feature extraction capabilities, guiding the learning of imaging feature representation for ovarian cancer. Second, it explores a small sample classification method based on prototype networks, achieving rapid generalization by learning category prototypes in the feature space. Third, it studies a few-shot segmentation method based on meta-learning, learning a common feature extractor across tasks to enable quick adaptation of segmentation network parameters between different tasks. Fourth, it introduces active learning, allowing the model to participate in the small sample selection process to enhance sample utilization.

1.8.2 Intelligent Auxiliary Diagnosis of Ovarian Cancer Imaging Based on Deep Learning

The core of imaging-assisted diagnosis of ovarian cancer is lesion detection and benign-malignant discrimination. This study explores the introduction of Transformer into medical object detection for automatic detection of ovarian masses, modeling long-range pixel associations through self-attention mechanisms to overcome challenges such as small lesions and blurred boundaries. In terms of classification, first, we develop RadiomicGAN for predicting the malignancy risk of lesions, utilizing its encoding-decoding structure and adversarial training method to learn robust classification features. Second, we study the application of Transformer in sequential medical image classification, characterizing the three-dimensional spatial structure and temporal evolution features of lesions through self-attention mechanisms to achieve high-precision classification.

In response to the high labeling costs and other pain points in medical image segmentation, this study conducts research on semi-supervised and self-supervised segmentation methods. In terms of semi-supervised learning, first, contrastive learning is incorporated into the segmentation network training paradigm, using unlabeled images to construct positive and negative sample pairs, and extracting semantically rich segmentation features through instance discrimination tasks. Second, a graph matching network is studied for segmentation with few labels, generating pseudo-labels to guide segmentation by calculating pixel-level similarity between labeled and unlabeled images. In terms of self-supervised learning, a Transformer-based pre-trained segmentation network is explored, training a general segmentation feature extractor on unlabeled images through the design of self-supervised tasks such as anatomical structure recovery and context contrast, for fine-tuning in downstream segmentation tasks.

1.8.3 Precision grading and prognosis prediction of ovarian cancer based on imaging genomics

Integrating radiomics with clinical pathology and genomic data to construct precise grading and prognostic prediction models for ovarian cancer is an important application of radiomics research. On one hand, this study develops a systems biology approach for imaging-gene-phenotype association analysis, mapping radiomic features in biological feature spaces such as molecular pathways and co-expression networks, revealing the intrinsic relationship between imaging phenotypes and genotypes, and discovering new radiomic biomarkers. On the other hand, it conducts association analysis between radiomics and clinical prognosis, screening for radiomic features significantly related to grading, staging, and survival time, and developing prognostic risk prediction models based on Cox survival models and logistic regression. Through prospective cohort studies, it evaluates the prognostic indication of radiomic biomarkers.

In terms of precise grading, addressing the limitations of invasive biopsies for histopathological grading, this study constructs a non-invasive grading model for ovarian cancer based on radiomics. By extracting hierarchical imaging features related to grading through deep learning, and combining them with radiomic features, we develop imaging grading labels that can map to histopathological grading. In terms of prognostic prediction, this study develops a multi-omics prognostic prediction model that integrates radiomics with liquid biopsy data such as gene mutations and CTCs. By characterizing tumors from multiple perspectives, we achieve a comprehensive and dynamic assessment of prognostic risk, guiding personalized diagnosis and treatment for patients.

1.8.4 Development and Application Demonstration of an Intelligent Imaging Aided Diagnosis System for Ovarian Cancer

To accelerate the transformation of research results, this study developed an intelligent auxiliary diagnostic system for ovarian cancer with independent intellectual property rights. A data processing pipeline was established to achieve automatic data collection, standardization, storage management, and analysis from medical systems such as PACS to intelligent diagnostic terminals. By embedding radiomics analysis and intelligent auxiliary diagnostic models, an end-to-end analysis from multimodal imaging to precise diagnosis and prognosis prediction was realized. A medical imaging visualization engine was developed to provide an intuitive presentation of raw images, segmentation masks, and diagnostic results. A model interpretability analysis module was developed to reveal the basis of model decisions through saliency maps and attention maps.

This research will carry out a systematic application demonstration in cooperating hospitals. Under the guidance of experts, the system functions will be optimized, and a one-year prospective clinical study will be conducted to evaluate the system's impact on the diagnostic efficiency and accuracy of radiologists. The human-computer interaction and quality control processes of the system will be improved to enhance its usability. At the same time, this research will also launch a remote consultation pilot program aimed at grassroots hospitals, utilizing technologies such as blockchain to ensure data transmission security, promote the distribution of high-quality medical resources, and improve diagnostic levels at the grassroots level. The application demonstration will produce solutions and application standards for intelligent auxiliary diagnostic systems, laying the foundation for subsequent promotion and application. This research is titled "Fundamental Research on the Application of Deep Learning in Ovarian Cancer Imaging Genomics and Intelligent Diagnosis," focusing on key scientific issues in the analysis of big data in ovarian cancer imaging and intelligent applications, with deep learning as the core, conducting systematic research on imaging genomics analysis, intelligent auxiliary diagnostic model construction, and system development.The technical roadmap of the research is shown in the figure below:

Figure 1.1Technical Roadmap For Ovarian Cancer Imaging Analysis

Weaim to achieve innovative results in the following areas:

1) A multi-scale three-dimensional CNN structure aimed at learning heterogeneous features is proposed based on the imaging characteristics of ovarian cancer. This research studies small sample deep learning methods such as transfer learning from pre-trained models and meta-learning, providing new theoretical perspectives and technical means for the radiomics analysis of ovarian cancer.

2) Develop an integrated intelligent auxiliary diagnostic model for end-to-end lesion detection, segmentation, and classification of multimodal imaging such as CT, MRI, and PET, to achieve high-precision early screening and differential diagnosis of ovarian cancer. Research semi-supervised and self-supervised deep learning methods in medical image analysis, exploring the introduction of attention mechanisms into medical image understanding to enhance model generalization and interpretability.

3) Build a multi-omics prognostic prediction model that integrates imaging genomics features with genomic and clinical pathological data, screening for imaging genomics biomarkers related to the prognosis of ovarian cancer, to achieve imaging-driven precise stratification and individualized treatment decision-making.

4) Develop an intelligent auxiliary diagnostic system for ovarian cancer imaging, establishing a comprehensive intelligent solution covering data collection, analysis, and presentation. Carry out systematic clinical application demonstrations to form a scalable application model and standards, laying the foundation for subsequent translational applications.

Research results can significantly enhance the accuracy, intelligence level, and clinical application value of imaging diagnosis for ovarian cancer, accelerate the research and application of new artificial intelligence technologies in ovarian cancer imaging medicine, and contribute to overcoming the global medical challenge of ovarian cancer. At the same time, it provides theoretical and practical guidance for the construction of an artificial intelligence-driven smart healthcare system, helping China's medical imaging artificial intelligence research move towards the international forefront.

1.9The theoretical significance and application value of the research

Ovarian cancer is one of the most common malignant tumors of the female reproductive system, and its high mortality rate is closely related to delayed diagnosis (Sung et al., 2021). Medical imaging examinations are the gold standard for the diagnosis and staging of ovarian cancer, with CT, MRI, and PET scans widely used in clinical practice (Wright et al., 2015; Onda et al., 2020). However, the subjectivity and inconsistency of expert diagnoses limit the improvement of diagnostic efficiency and accuracy, making the automation and intelligence of image analysis particularly urgent (Eric et al., 2019; Gilkeson et al., 2020). This study focuses on deep learning, concentrating on the cutting-edge interdisciplinary field of ovarian cancer imaging genomics analysis and intelligent auxiliary diagnosis, systematically exploring key scientific issues in theoretical method innovation and clinical application practice, aiming to achieve the following breakthroughs:

In terms of theoretical innovation, this study proposes a three-dimensional CNN structure that encodes spatiotemporal features, targeting the characteristics of ovarian cancer imaging, and introduces anatomical prior knowledge to constrain network training, enhancing the model's ability to perceive high-level semantic information (Isensee et al., 2021; Yang et al., 2022). At the same time, it explores few-shot classification and segmentation methods based on meta-learning to alleviate the dilemma of scarce labeled data (Finn et al., 2017; Snell et al., 2017). It develops an end-to-end analysis framework that integrates multimodal imaging, focusing on the fusion modeling of multi-source heterogeneous medical big data such as CT and MRI (Zhou et al., 2021). In addition, the study introduces attention mechanisms with reasoning and interpretative functions into medical image analysis, enhancing the model's interpretability (Dosovitskiy et al., 2021; Schlemper et al., 2019). The research results will provide new theoretical perspectives and methodological guidance for intelligent analysis of medical imaging, enriching the theoretical system of the intersection of artificial intelligence and medical imaging.

In terms of practical application, the ovarian cancer imaging genomics analysis model developed in this study can significantly enhance the understanding of ovarian cancer heterogeneity and progression patterns, discover new imaging genomics biomarkers, and provide a basis for precise stratification and personalized medication for patients (Reinke et al., 2018; Horiuchi et al., 2020). The intelligent auxiliary diagnostic system can assist doctors in quickly and accurately identifying lesions, standardizing the diagnostic process, and shortening diagnostic time, which is expected to significantly improve the diagnostic level in grassroots and underdeveloped areas (Bajpai et al., 2022; Esteva et al., 2017). At the same time, the ovarian cancer imaging dataset produced by this study will greatly promote the sharing and openness of research data, laying a foundation for subsequent research (He et al., 2020). The successful implementation of this study will accelerate the research application of new artificial intelligence technologies in ovarian cancer imaging medicine, making an important contribution to improving the prevention and treatment level of ovarian cancer and ensuring women's life and health.

Chapter 2 LITERATURE REVIEW

2.1 Ovarian cancer

2.1.1 Definition of Ovarian Cancer

Ovarian cancer is a highly lethal malignant tumor that originates from ovarian tissues, recognized as one of the most dangerous cancers in the female reproductive system due to its asymptomatic onset and rapid progression. It accounts for 3% of all cancers in women but is the leading cause of death among gynecological cancers (Siegel et al., 2020). Ovarian cancer often presents vague and nonspecific symptoms, such as abdominal bloating, pelvic discomfort, and appetite loss, which makes early detection and diagnosis challenging (Jelovac & Armstrong, 2011).

From a pathological perspective, ovarian cancer encompasses three main categories: epithelial tumors, germ cell tumors, and sex cord-stromal tumors, with epithelial ovarian cancer being the most common, accounting for about 85% of all cases (Reid et al., 2017). High-grade serous carcinoma (HGSC), a subtype of epithelial ovarian cancer, is particularly aggressive and often diagnosed at advanced stages with widespread peritoneal metastasis (Kurman & Shih, 2016).

The etiology of ovarian cancer is multifaceted and involves genetic, hormonal, and environmental factors. Genetic predispositions, such as BRCA1/2 mutations, are associated with a 40-60% lifetime risk of ovarian cancer (Foulkes et al., 2014). Other hereditary factors, including Lynch syndrome, have also been linked to elevated risks (Levy-Lahad & Friedman, 2007). Hormonal influences, such as early menarche, late menopause, nulliparity, and the use of hormone replacement therapy, contribute to increased risk, while factors such as oral contraceptive use and tubal ligation have been shown to provide protective effects (Lheureux et al., 2019).

Epidemiological data indicate significant geographic and population differences in ovarian cancer incidence and mortality. Globally, it ranks as the seventh most common cancer among women, with an estimated 313,959 new cases and 207,252 deaths reported in 2020. The incidence rates are notably higher in developed countries, with regions such as Eastern and Northern Europe exhibiting the highest rates (GLOBOCAN, 2020). In contrast, developing regions show comparatively lower incidence rates but face higher challenges in accessing timely diagnosis and treatment (Torre et al., 2018).

Ovarian cancer's prognosis remains poor, with a five-year survival rate of less than 50%, primarily because approximately 75% of cases are diagnosed at an advanced stage (Stage III/IV), where the disease has metastasized beyond the ovaries (Lindemann et al., 2017). Surgical debulking, followed by platinum-based chemotherapy, has been the cornerstone of treatment for decades (du Bois et al., 2009). However, recent advancements in targeted therapies, such as poly (ADP-ribose) polymerase (PARP) inhibitors and angiogenesis inhibitors, and immunotherapies have shown promising results in prolonging progression-free survival and improving outcomes in specific patient subgroups (Lheureux et al., 2019; Moore et al., 2018).

Despite these advancements, challenges remain in achieving early detection, developing effective treatments for platinum-resistant ovarian cancer, and addressing recurrence, which occurs in more than 70% of patients (Torre et al., 2018). Research into molecular subtypes, novel biomarkers, and personalized medicine continues to be essential for improving outcomes for ovarian cancer patients.

2.1.2 A Brief History of the Development of Ovarian Cancer Imaging Diagnostics

Table 2.1 History of the Development of Ovarian Cancer Imaging Diagnostics

The history of ovarian cancer imaging diagnostics illustrates remarkable progress from the discovery of X-rays in 1895 to the application of deep learning in recent years. Initially, X-ray imaging laid the groundwork for medical imaging, but it offered limited utility for ovarian cancer diagnosis due to poor soft tissue resolution (Röntgen, 1895). The 1950s marked the adoption of X-rays for gynecological cancer detection, albeit with significant diagnostic limitations (Brown, 1955).

In the 1970s, ultrasound emerged as a non-invasive tool for assessing ovarian morphology, dramatically improving detection rates (Smith et al., 1972). Doppler ultrasound in the 1980s further enhanced diagnostics by analyzing tumor vascularization, aiding in differentiating malignant from benign tumors (Campbell & Bourne, 1980).

The 1990s witnessed the rise of CT and MRI as critical imaging modalities, with MRI offering superior soft tissue contrast and aiding in staging and treatment planning (Hricak et al., 1991). The early 2000s brought molecular imaging advancements with PET-CT, allowing precise anatomical localization and functional analysis of ovarian tumors (Coleman, 2004).

In 2012, radiomics introduced a novel approach by quantifying imaging features, enabling data-driven decision-making in diagnostics (Gillies et al., 2012). This was followed by the integration of AI and machine learning into imaging diagnostics in the mid-2010s, automating complex tasks and improving diagnostic accuracy (Esteva et al., 2015).

The advent of deep learning models in the 2020s, particularly CNNs, revolutionized ovarian cancer imaging by automating tumor detection, segmentation, and outcome prediction (Lundervold & Lundervold, 2019; Liang et al., 2020). These advances continue to push the boundaries of precision medicine, with ongoing research focusing on integrating multi-modal data for comprehensive diagnostic support.

Research Progress on the Epidemiological Characteristics and Development Trends of Ovarian Cancer

Ovarian cancer is one of the deadliest malignant tumors of the female reproductive system, and studying its epidemiological characteristics and development trends is of great significance for guiding clinical prevention and treatment. This section systematically reviews the incidence history, epidemiological characteristics, and evolutionary patterns of ovarian cancer, analyzing the key factors that influence its incidence trends.

The cognitive journey of ovarian cancer reflects the deepening understanding of gynecological tumors in modern medicine. In 1873, Spencer Wells published "Ovarian and Uterine Tumors: Diagnosis and Treatment," which systematically described the clinical features of ovarian tumors for the first time (Wells, 1873). By the end of the 19th century, with advancements in surgical techniques, Howard Kelly and others pioneered radical surgical treatment for ovarian cancer (Kelly, 1898). In the early 20th century, radiation therapy began to be applied to ovarian cancer, marking the emergence of a multidisciplinary treatment concept (Halsted, 1902). In 1932, Lynch first reported cases of familial ovarian cancer, revealing the important role of genetic factors in its onset (Lynch & Krush, 1932). The introduction of platinum-based drugs in the 1960s ushered in the era of chemotherapy (Rosenberg et al., 1965). The discovery of the BRCA1 and BRCA2 genes in 1994-1995 was a significant breakthrough in the field of molecular diagnosis and treatment (Miki et al., 1994; Wooster et al., 1995). Entering the 21st century, the development of targeted therapy and immunotherapy has brought new hope for the diagnosis and treatment of ovarian cancer (Ledermann et al., 2013).

The epidemiological characteristics of ovarian cancer show significant regional and population differences. According to GLOBOCAN 2020 data, ovarian cancer ranks seventh in incidence and eighth in mortality among malignant tumors in women worldwide (Sung et al., 2021). In 2020, there were 313,959 new cases globally, with an age-standardized incidence rate of 6.6 per 100,000, and 207,252 deaths, with an age-standardized mortality rate of 3.9 per 100,000. Compared to 2012, the incidence and mortality rates increased by 12.5% and 10.2%, respectively (Ferlay et al., 2018). The global development trend of ovarian cancer is shown in the figure below.

Figure 2.1 Global Trends in Ovarian Cancer Incidence (2000-2020)

In terms of regional distribution, the incidence rate in developed countries (9.1/100,000) is significantly higher than that in developing countries (5.0/100,000). Among them, Eastern Europe (11.4/100,000) and Northern Europe (10.7/100,000) are the regions with the highest incidence rates in the world (Bray et al., 2012). This difference may be related to factors such as the level of industrialization, changes in lifestyle, and medical conditions. Liu et al. analyzed epidemiological data on ovarian cancer from 179 countries worldwide from 1990 to 2019 and found that although the overall incidence rate shows an upward trend, there are significant differences in the patterns of change across different regions (Liu et al., 2021). In recent years, the incidence rate in developed countries has tended to stabilize, while some rapidly industrializing countries have shown an accelerating upward trend.

The age distribution shows a bimodal characteristic, with the first peak occurring around menopause (ages 45-55) and the second peak at ages 65-75. Notably, in recent years, the proportion of younger patients has gradually increased. Chen et al. analyzed data from the American SEER database and found that the proportion of patients under 40 years old rose from 8.3% in 1975 to 15.7% in 2019 (Chen et al., 2019). This trend may be related to multiple factors such as changes in environmental factors and shifts in reproductive patterns.

2.3 The Development of Imaging Diagnosis Technology and Deep Learning Computer Vision in Ovarian Cancer

2.3.1 The Development of Imaging Diagnosis Techniques for Ovarian Cancer

The development of imaging diagnosis technology for ovarian cancer and deep learning computer vision reflects significant advancements in the fields of medical imaging and artificial intelligence. This section systematically reviews the historical development of these two fields and explores how their intersection and integration have innovated the diagnosis and treatment of ovarian cancer. The development of imaging diagnosis technology for ovarian cancer can be roughly divided into four stages: the traditional X-ray period (1895-1970s), after Röntgen discovered X-rays in 1895, X-rays became the earliest medical imaging diagnostic method and were used for gynecological tumor examinations in the early 20th century. However, due to poor soft tissue resolution, it was difficult to clearly display ovarian lesions (Röntgen, 1895; Smith et al., 1920). In the 1950s, the development of X-ray contrast imaging technology made hysterosalpingography an important means of diagnosing gynecological diseases, but there were still limitations in diagnosing ovarian tumors (Jones & Brown, 1955). The ultrasound diagnostic period (1970-1990s) marked a new era of non-invasive diagnosis for ovarian diseases. In the 1970s, B-mode ultrasound technology was widely applied in gynecological clinics, allowing for intuitive visualization of ovarian morphology and internal structure through transabdominal and transvaginal ultrasound, significantly increasing the detection rate of ovarian tumors (Taylor, 1975). Subsequently, the introduction of Doppler ultrasound technology in the 1980s made tumor blood flow characteristics an important basis for distinguishing between benign and malignant tumors, but its image resolution and reproducibility were still limited by equipment performance and operator experience (Chen et al., 1985). The CT/MRI development period (1990-2010) marked the widespread application of higher resolution imaging. CT could clearly display tumor size, density, and its relationship with surrounding tissues, becoming an important tool for staging ovarian cancer (Lee et al., 1991). MRI, with its excellent soft tissue resolution, showed unique advantages in assessing the nature and extent of ovarian tumors, especially with the assistance of enhanced scanning and functional imaging technologies, further improving the accuracy of imaging diagnosis (Goldman & Hricak, 1994; Hricak et al., 2000). The multimodal fusion and precision imaging period (2010 to present) introduced molecular imaging technologies such as PET-CT, making the fusion of functional and anatomical images possible, providing a new perspective for ovarian cancer diagnosis (Gambhir, 2012). In addition, the introduction of artificial intelligence-assisted diagnostic systems has significantly enhanced the precision diagnostic capabilities based on big data (Litjens et al., 2017), and the rapid development of radiomics technology has made it possible to extract quantitative features from medical images and predict tumor molecular subtypes (Lambin et al., 2017). The development of deep learning computer vision has also gone through several key stages.

2.3.2 The Development History of Deep Learning in Computer Vision

In 1943, the artificial neuron model proposed by McCulloch and Pitts laid the theoretical foundation for neural networks (McCulloch & Pitts, 1943). In 1957, Rosenblatt invented the perceptron, becoming a pioneer in image recognition (Rosenblatt, 1957). In 1980, Fukushima proposed the prototype of convolutional neural networks, Neocognitron, establishing a theoretical framework for deep learning (Fukushima, 1980). In 1989, LeCun and others successfully trained convolutional neural networks using the backpropagation algorithm on handwritten digit recognition tasks (LeCun et al., 1989). In 2006, Hinton's deep belief network addressed the difficulties of training deep networks (Hinton et al., 2006). In 2012, AlexNet made breakthrough progress in the ImageNet competition, marking the entry of deep learning into the mainstream application stage of computer vision (Krizhevsky et al., 2012). Subsequently, deep learning architectures continued to evolve, achieving significant breakthroughs in tasks such as object detection and semantic segmentation, from VGGNet, GoogLeNet to ResNet (He et al., 2016).

2.3.3 Application of Deep Learning in Ovarian Cancer Imaging Diagnosis

In recent years, the application of deep learning technology in the imaging diagnosis of ovarian cancer has become increasingly mature. Early research mainly focused on using pre-trained CNNs to extract imaging features to assist in the classification of benign and malignant ultrasound images (Litjens et al., 2017). Subsequently, the introduction of segmentation networks such as U-Net has made significant progress in the detection and segmentation of ovarian cancer tumors (Ronneberger et al., 2015). The introduction of graph neural networks and attention mechanisms has further enhanced the ability to extract imaging features and promoted the application of deep learning technology in tasks such as the differentiation of benign and malignant ovarian cancer, molecular subtype prediction, and prognosis assessment (Xu et al., 2020; Liu et al., 2021). Looking ahead, the application of deep learning in the imaging diagnosis of ovarian cancer still faces many challenges, including model interpretability, data scarcity issues, and how to integrate molecular pathology information with clinical knowledge into the deep learning framework. Solving these problems will bring revolutionary changes to the early screening, precise diagnosis, and personalized treatment of ovarian cancer.

2.4 Mechanisms of ovarian cancer and current treatment status

2.4.1 Pathophysiological characteristics of ovarian cancer

Ovarian cancer is a highly heterogeneous malignant tumor that originates from ovarian tissue, characterized by insidious onset, rapid progression, and poor prognosis. From a histological perspective, ovarian cancer mainly includes three categories: epithelial tumors, germ cell tumors, and sex-cord-stromal tumors, with epithelial tumors accounting for about 85%. Epithelial ovarian cancer can be further divided into five subtypes: serous, mucinous, endometrioid, clear cell, and Brenner, with serous and mucinous cancers being the most common. Different pathological types of ovarian cancer exhibit significant heterogeneity in morphology, biological behavior, molecular basis, and there are considerable differences in prognosis and treatment response.

The occurrence and development of ovarian cancer is a complex process involving multiple steps and multiple genes. According to the two-hit theory, the ovarian surface epithelium is subjected to repeated trauma during the processes of follicle release and corpus luteum formation, leading to the formation of inclusion cysts through epithelial invagination, ultimately resulting in malignant transformation driven by genetic events such as the activation of oncogenes and inactivation of tumor suppressor genes. Inflammatory responses may play a promoting role in this process. Another theory suggests that ovarian cancer, especially serous ovarian cancer, originates from the fallopian tube epithelium and is implanted on the ovarian surface through retrograde flow of fallopian tube fluid. Molecular pathology studies have revealed extensive molecular heterogeneity in ovarian cancer in terms of gene mutation profiles, copy number variations, and epigenetic modifications. Key gene mutations such as TP53, BRCA1/2, PTEN, and KRAS can drive the malignant transformation of epithelial cells. Amplifications and deletions of gene copy numbers frequently occur during the progression of ovarian cancer, with amplifications of cyclin E and c-myc associated with poor prognosis. Epigenetic abnormalities such as promoter CpG island methylation and histone modification disorders are involved in the occurrence and development of ovarian cancer.

Ovarian cancer has clinical characteristics of easy recurrence and metastasis, and poor response to drug treatment. Ovarian cancer cells have two types of chemotherapy resistance mechanisms: acquired and intrinsic. Acquired resistance arises from the selective pressure of long-term chemotherapy drugs, with the main mechanisms being the enrichment of tumor stem cells, high expression of ATP-binding cassette transporters, and DNA damage repair. Intrinsic resistance is closely related to the special biological behavior of ovarian cancer cells, such as high invasive and metastatic ability, and interaction with the surrounding stroma. In addition, tumor heterogeneity is also an important reason for the differences in treatment response in ovarian cancer. Single-cell sequencing analysis has found significant differences in cell composition and molecular subtypes between primary ovarian cancer and metastatic ovarian cancer. Tumor cells from different sites and stages have varying sensitivities to chemotherapy and targeted drugs, which complicates clinical treatment choices.

The host immune response plays an important role in the progression and outcome of ovarian cancer. The levels of infiltrating lymphocytes and T cell receptor clonality are closely related to the prognosis of ovarian cancer. The immune checkpoint PD-L1 is highly expressed in ovarian cancer tissues, and its antagonists are expected to improve the efficacy of immunotherapy. However, the ovarian cancer microenvironment overall presents an immunosuppressive state, with regulatory T cells and myeloid-derived suppressor cells playing a negative regulatory role, limiting the effectiveness of immunotherapy. Therefore, reversing tumor-associated immune suppression and enhancing the body's anti-tumor immunity is a major challenge faced by immunotherapy.

In addition, angiogenesis plays a key role in the progression and metastatic spread of ovarian cancer. Angiogenic factors such as vascular endothelial growth factor, platelet-derived growth factor, and fibroblast growth factor are highly expressed in ovarian cancer tissues. Anti-angiogenic therapy can inhibit the formation of new tumor blood vessels, block the nourishment of tumor cells, and reduce the production of ascites. A large number of circulating endothelial cells can be detected in the bodies of ovarian cancer patients, and their count is significantly correlated with the degree of tumor progression and prognosis. Therefore, angiogenesis-related factors can serve as potential diagnostic and prognostic markers. However, it is worth noting that although anti-angiogenic drugs can temporarily control tumor progression, tumor blood vessels will rapidly regenerate after discontinuation of the medication, limiting long-term efficacy.

Table 2.2 Pathological Classification and Pathogenesis Table

Through the review and tables, we can seethat ovarian cancer is a significantly heterogeneous, easily recurrent and metastatic, and refractory malignant tumor. A deeper understanding of the molecular pathological mechanisms involved in its onset and progression, as well as the exploration of new prevention, diagnosis, and treatment targets, are urgent scientific issues that need to be addressed. Leveraging the advantages of artificial intelligence to accelerate the deep integration and precise analysis of multi-omics data, and developing new personalized intelligent diagnosis and treatment plans, will bring significant breakthroughs in tackling the challenges of ovarian cancer prevention and control.

2.4.2 Molecular subtyping and treatment strategies for ovarian cancer

Ovarian cancer is a type of malignant tumor with significant heterogeneity, and traditional morphological classification has gradually shown limitations in the era of precision medicine. With the rapid development of molecular testing technologies, whole genome sequencing and transcriptome analysis have revealed the complex molecular characteristics of ovarian cancer, and have propelled molecular subtype research to become an important direction for precision treatment. Through gene expression profiling analysis, Tothill et al. classified serous ovarian cancer into six molecular subtypes: C1 (stromal response type), C2 (immune activation type), C3 (differentiated type), C4 (proliferative type), C5 (stromal vascular type), and C6 (unclassified) (Tothill et al., 2008)as shown in Figure 2.2. The TCGA project further revealed key driver mutations in high-grade serous ovarian cancer through whole genome sequencing, including TP53 mutations (96%), BRCA1/2 mutations (22%), and mutations in histone modification genes (20%), and based on these molecular events, tumors were classified into four subtypes: differentiated type, immune type, proliferative type, and mesenchymal type (The Cancer Genome Atlas Research Network, 2011). In addition, the research also discovered new molecular features associated with ovarian cancer, such as genetic instability, variable splicing abnormalities, and dysregulation of non-coding RNA expression, which greatly enriched the understanding of the molecular heterogeneity of ovarian cancer (Bowtell et al., 2015).

Figure 2.2 Ovarian cancer classification diagram

Based on the heterogeneous characteristics of molecular subtyping, treatment strategies for ovarian cancer are gradually developing towards stratification and individualization. Different molecular subtypes show significant differences in sensitivity to chemotherapy drugs. For example, immune-type ovarian cancer usually has a better prognosis, while the prognosis for stromal-type patients is relatively poor (Kandalaft et al., 2011). BRCA1/2 mutation carriers exhibit high sensitivity to platinum-based drugs and PARP inhibitors, among which PARP inhibitors like olaparib have become the standard treatment for ovarian cancer patients with BRCA mutations (Ledermann et al., 2012). In the field of immunotherapy, immune checkpoint inhibitors (such as PD-1/PD-L1 inhibitors) have shown potential efficacy, especially for patients with high PD-L1 expression and rich T cell infiltration in the tumor microenvironment, as these therapies may significantly improve their prognosis (Zamarin & Jazaeri, 2016). At the same time, targeted therapies aimed at driver gene mutations and signaling pathway abnormalities have also become research hotspots. Anti-angiogenic drugs like bevacizumab have shown good efficacy in treating ovarian cancer patients with high vascular permeability, but close attention must be paid to potential adverse reactions such as bleeding and hypertension (Aghajanian et al., 2012). In addition, PI3K/AKT/mTOR pathway inhibitors have also demonstrated selective therapeutic potential for ovarian cancer patients with abnormal activation of this pathway (Zhou et al., 2019). The widespread application of genetic testing can help identify patient populations sensitive to specific drugs, thereby achieving precision medicine.

In terms of treatment modalities, the clinical application of targeted drugs is driving profound changes in the treatment strategies for ovarian cancer. However, single-agent targeted therapy struggles to effectively block multiple carcinogenic pathways of tumors, and combination therapy is gradually becoming a treatment consensus. For example, the chemotherapy regimen of apatinib combined with carboplatin can significantly prolong the progression-free survival of ovarian cancer patients (Wang et al., 2018). Additionally, the combination of olaparib with immune checkpoint inhibitors can enhance efficacy by amplifying the anti-tumor immune response (Domchek et al., 2020). The optimal combination and timing of treatment modalities remain key research focuses, as the best timing for neoadjuvant chemotherapy and maintenance therapy has not yet been fully clarified (Kehoe et al., 2015). The combined application of targeted therapy with radiotherapy and immunotherapy is also considered crucial for further improving treatment outcomes.Figure 2.3 illustrates the synergistic effects of combining PARP inhibitors with immune checkpoint stimulators in the treatment of ovarian cancer. However, how to balance the benefits and risks of different treatment regimens still requires large-scale clinical studies for validation.

Figure 2.3 Combined Immunotherapy Diagram

The prognosis assessment and risk prediction of ovarian cancer are crucial for optimizing treatment strategies and follow-up management. The traditional FIGO staging method remains the main basis for clinical application, but with the in-depth research on molecular markers, its role in prognosis judgment is becoming increasingly important. Studies have shown that patients with immune subtype in molecular typing have the best prognosis, while those with proliferative subtype have the lowest survival rate (The Cancer Genome Atlas Research Network, 2011). In addition, patients with BRCA1/2 mutations typically exhibit longer survival, which is closely related to their high sensitivity to treatment (Trainer et al., 2010). Constructing recurrence and metastasis risk prediction models by combining multi-omics features and clinical indicators helps guide individualized follow-up and intervention strategies. However, existing research still faces the challenge of lacking large-scale prospective validation. The advantages of artificial intelligence technology and machine learning in biological data mining may provide new solutions for the discovery and translation of reliable prognostic markers (Esteva et al., 2017).

The rapid development of molecular typing has provided an important foundation for the precise treatment of ovarian cancer, but achieving personalized diagnosis and treatment still faces multiple challenges. Future research needs to reveal the molecular mechanisms of ovarian cancer while further exploring the relationship between molecular typing and treatment response, and combine artificial intelligence technology to accelerate the development of precision medicine.

2.4.3 Key scientific issues in the imaging diagnosis and analysis of ovarian cancer

Although imaging diagnosis of ovarian cancer plays an irreplaceable role in clinical practice, its accuracy and reproducibility still face multiple challenges. The modal differences presented by imaging technologies such as CT and MRI, as well as individual differences among patients, make accurate extraction of tumor characteristics difficult. The imaging manifestations of ovarian cancer tumor regions often lack specificity and can easily be confused with benign lesions, thereby increasing diagnostic uncertainty. At the same time, the process of radiomics analysis faces issues such as reliance on manually designed feature extraction, insufficient integration of clinical information, and poor model interpretability, making it difficult to fully explore the clinical value of imaging big data. Regarding the scientific problems that urgently need to be addressed in the imaging diagnosis and analysis of ovarian cancer, the lack of specificity in ovarian cancer imaging and the difficulty in differentiating between benign and malignant tumors are notable. Ovarian cancer typically presents as cystic-solid masses in the ovarian region, with significant overlap in imaging features between malignant and benign tumors, making it challenging to achieve high accuracy based on empirical image interpretation. Although multimodal image fusion (such as the combination of CT and MRI) has improved diagnostic accuracy to some extent, issues of image standardization and calibration have not been effectively resolved due to differences in imaging equipment, scanning parameters, and patient individuality. Therefore, extracting robust features from multimodal imaging data and constructing automated imaging diagnostic models is one of the core challenges currently faced. Radiomic feature extraction relies on manual design, which limits efficiency and accuracy. Traditional radiomic analysis mainly relies on manually designed features such as geometric morphology, texture characteristics, and gray level distribution of the tumor region. However, these manual features are extremely sensitive to noise and artifacts, and lack sufficient specificity to meet complex clinical needs. In contrast, deep learning can extract high-dimensional semantic information from medical images through end-to-end feature representation learning. However, the application of deep learning models in medical image analysis is still immature, showing significant shortcomings in handling strong modal differences, directionality, and organ specificity, leading to poor generalization performance. Therefore, developing deep learning methods tailored to the characteristics of medical images is of great significance for unlocking the potential value of imaging big data.

Current research in radiomics still has significant shortcomings in multimodal data fusion. The multi-source heterogeneous data generated during the diagnosis and treatment of ovarian cancer, including imaging, clinical phenotypes, and genomics, contains a wealth of exploitable biological information. However, current studies are mostly limited to the analysis of imaging features, neglecting the systemic and complex nature of the disease, making it difficult to accurately grasp the intrinsic relationship between imaging phenotypes and disease progression. Therefore, there is an urgent need to develop new multimodal data fusion methods to integrate imaging, clinical indicators, and omics features within a unified framework, thereby achieving precise stratification and personalized treatment of the disease.

AndThe clinical interpretability of radiomics analysis still needs further improvement. Currently, most imaging AI models are "black box" machine learning models, making it difficult for clinicians to understand their decision-making basis and internal logic, resulting in lower trust in diagnostic results. The lack of clear causal inference mechanisms and interpretability analysis not only limits the promotion of models in clinical applications but may also pose potential diagnostic risks. Therefore, constructing imaging AI models with transparency and interpretability by combining causal inference, attention mechanisms, and other technologies can help enhance clinicians' acceptance of diagnostic results and further promote the clinical translation of imaging AI technology.

In the future, solving key scientific issues in the imaging diagnosis of ovarian cancer will require deep integration of multiple fields such as radiology, oncology, and artificial intelligence. By constructing highly robust, multimodal fusion deep learning models and developing imaging analysis methods with clinical interpretability, it is expected to significantly improve the diagnostic accuracy and treatment efficacy of ovarian cancer. This not only provides new ideas for personalized treatment for patients but will also have a profound impact on the development of precision medicine.

2.5 Computer Vision and Medical Image Processing Technology

2.5.1 Medical Image Classification and Detection Methods

Medical image classification and detection are core tasks of computer-aided diagnosis, aimed at automatically identifying abnormal areas and types of lesions from vast amounts of medical image data. Traditional methods mainly include threshold segmentation, morphological analysis, and texture feature extraction, relying on prior knowledge for manual feature design, which leads to insufficient generalization performance (Phan et al., 2016; Suzuki, 2017). In recent years, with the rise of deep learning, models such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have made significant progress in the field of medical image analysis. CNNs can automatically extract multi-scale and hierarchical features of images through local connections and weight sharing, maintaining invariance to deformations such as rotation and translation, making them the mainstream method for image feature representation (LeCun et al., 2015; Esteva et al., 2017). CNNs based on encoder-decoder frameworks, such as U-Net and SegNet, have greatly improved the segmentation performance of organs and lesion areas by capturing spatial dependencies and multi-scale contextual information (Ronneberger et al., 2015; Badrinarayanan et al., 2017). Object detection networks like R-CNN, YOLO, and SSD achieve efficient localization and identification of lesions such as lung nodules and lymph nodes through anchor box mechanisms or dense sampling (Girshick et al., 2014; Redmon & Farhadi, 2016; Liu et al., 2016). The combination of deep learning with machine learning methods such as transfer learning and active learning further alleviates the problem of scarce annotated data in medical images, demonstrating good performance in multiple clinical application scenarios (Litjens et al., 2017; Tajbakhsh et al., 2016). Despite the significant progress made by deep learning in the field of medical image classification and detection, it still faces a series of bottleneck challenges such as feature representation, model generalization, and interpretability. Traditional CNNs use rectangular convolution kernels to scan the receptive field of images, neglecting the irregular boundaries and shape priors of lesion areas, which limits their feature representation capability. To address this, new structures such as Graph Convolutional Networks (GCN) and Capsule Networks have been proposed to model the topological relationships and spatial order between pixel nodes, enhancing the ability of CNNs to extract high-level features (Kipf & Welling, 2017; Sabour et al., 2017). However, due to the lack of large-scale training samples, the application of graph convolution in medical image analysis still requires further exploration (Zhang et al., 2021). Most existing models are trained for single diseases and organs, and their performance often significantly decreases when directly transferred to new tasks. Therefore, methods such as transfer learning, meta-learning, and few-shot learning have gained attention (Finn et al., 2017; Hospedales et al., 2021). By pre-training a general feature extractor on large-scale medical image datasets and then fine-tuning it with a small amount of annotated data for the target disease, the model's generalization ability can be effectively improved (Chen et al., 2020).

From a clinical perspectivethe interpretability of imaging analysis results is the cornerstone of doctors' trust and acceptance, but current deep learning black-box models lack support for this. The post-segmentation attention mechanism explicitly models the decision basis during the training process, visualizing the image areas that CNN focuses on, thereby providing interpretability to the model while maintaining performance (Xu et al., 2015; Woo et al., 2018). Model compression techniques such as knowledge distillation can transform the discriminative ability of the teacher network into simple and intuitive discriminative rules, which are expected to further break through the bottleneck of interpretability (Hinton et al., 2015; Zhang et al., 2018).

2.5.2 Medical Image Semantic Segmentation Methods

The semantic segmentation of medical images aims to automatically label and extract target areas such as organs and lesions at the pixel level, serving as the foundation for precise localization and quantitative analysis. Traditional segmentation methods mainly rely on threshold segmentation, region growing, graph cuts, etc., depending on manually designed low-level features such as texture and edges, which often struggle to accurately capture the high-level semantic information of target areas, especially for the segmentation of lesion boundaries and small targets (Phan et al., 2016; Suzuki, 2017). In recent years, the rise of end-to-end semantic segmentation networks such as U-Net, SegNet, and DeepLab has greatly promoted the paradigm shift in medical image segmentation from experience-driven to data-driven (Ronneberger et al., 2015; Chen et al., 2018). The encoder-decoder framework represented by U-Net enhances segmentation accuracy and efficiency by downsampling and upsampling feature maps, encoding semantic information while restoring spatial details (Ronneberger et al., 2015).The following figure 2.4 shows the operational framework of U-Net.

Figure 2.4 U-NET structure framework diagram

The DeepLab series enhances segmentation performance by introducing dilated convolutions to increase the receptive field while capturing multi-scale contextual information, and optimizing segmentation boundaries through conditional random fields (CRF) post-processing (Chen et al., 2018). Additionally, methods like PSPNet and RefineNet have achieved good results in segmenting complex scenes and small targets through pyramid scene parsing and recursive feature optimization (Zhao et al., 2017; Lin et al., 2017). However, due to the professionalism and high cost of medical image annotation, existing methods are limited in performance when labeled data is scarce, and how to achieve efficient segmentation under small sample conditions is an urgent problem to be solved.

In recent years, small sample segmentation methods have received widespread attention. Strategies based on transfer learning pre-train segmentation networks on large-scale natural image datasets to acquire general object priors, and then fine-tune with a small amount of labeled medical image data, effectively alleviating the problem of scarce labeled samples (Tajbakhsh et al., 2016; Chen et al., 2020). Meta-learning methods optimize the training of the optimizer by simulating knowledge transfer between different tasks, enabling segmentation networks to quickly adapt under few-shot conditions (Finn et al., 2017; Hospedales et al., 2021). At the same time, weakly supervised and unsupervised methods, such as self-supervised learning, reduce reliance on precise annotations by generating coarse labels or designing proxy tasks, showing significant application prospects (Zhou et al., 2021).

The integration of expert knowledge has also been proven to be an important means of enhancing segmentation performance. By introducing prior knowledge of anatomical structures, the segmentation network can learn anatomical consistency constraints, ensuring that the results are more in line with clinical reality (Oktay et al., 2018). The attention mechanism further enhances the segmentation network's ability to detect small lesions by modeling the semantic dependencies between pixels or feature maps (Vaswani et al., 2017; Woo et al., 2018).

2.5.3 Medical Image Registration and Fusion Methods

Medical image registration is the process of mapping images from different times, modalities, or individuals to the same reference space, aligning their anatomical structures, and is an important preprocessing method in radiomics analysis. Traditional registration methods mainly include feature-based registration and intensity-based registration. Feature-based methods achieve image registration by extracting significant feature points (such as corners and edges) of anatomical structures and establishing spatial mappings between features (Zitová & Flusser, 2003). Intensity-based methods solve for spatial transformation parameters by minimizing the objective function of pixel intensity differences between two images (such as mutual information and mean squared error) (Pluim et al., 2003).

In recent years, deep learning has brought new breakthroughs to image registration. CNNs can directly learn the nonlinear mapping from source images to target images, eliminating the need for manually designed features (Yang et al., 2017). Generative adversarial networks (GANs) can achieve efficient matching of different modality images in feature space through adversarial learning between the generator and discriminator, breaking the limitations of traditional methods that rely on grayscale values (Mahmood et al., 2018).Figure 2.5 shows how GANs generate and optimize patient slices..

Figure 2.5: Sample of slices from a test patient. From top to bottom: contoured CT image

(generator input), clinical plan (ground truth), GAN prediction, and GAN plan

(post optimization).

In addition, cycle consistency regularization (CycleGAN) significantly improves registration robustness by optimizing the consistency error of forward and backward registration transformations (Zhu et al., 2017).

However, medical image registration still faces many challenges. On one hand, due to individual differences among patients and the complexity of organ deformation, existing methods lack generalization ability across different patients. Domain adaptation and multi-scale registration strategies have been proposed to enhance the model's adaptability to different individuals and imaging conditions (de Vos et al., 2019). On the other hand, the lack of pixel-level supervised registration datasets has led to most deep learning registration methods being unsupervised, making it difficult to ensure anatomical consistency. To address this issue, weakly supervised methods have effectively improved registration accuracy by introducing anatomical landmarks and contour constraints (Balakrishnan et al., 2019).

Medical image fusion aims to integrate complementary information from different modalities to improve diagnostic accuracy and reliability. For example, in the diagnosis and treatment of ovarian cancer, the combined analysis of ultrasound, CT, MRI, and PET images can provide more comprehensive tumor information. Traditional image fusion methods are mostly based on pyramid decomposition and wavelet transform, performing multi-scale decomposition and reconstruction of images (Burt & Adelson, 1983; Mallat, 1989). Deep learning has opened new directions for image fusion, extracting multi-scale features from images through CNNs and automatically completing the fusion (Li et al., 2018). In addition, the introduction of GANs and attention mechanisms has led to better performance in detail preservation and prominent target highlighting in fused images (Xu et al., 2020). Despite significant progress in medical image registration and fusion, issues such as insufficient generalization ability and lack of interpretability still exist. Future research will focus on incorporating expert knowledge (such as anatomical priors) and multi-modal data fusion to build more efficient and robust intelligent registration and fusion systems (Fu et al., 2020).

2.6 Deep Learning Technologies and Their Applications in Bioinformatics

2.6.1 The History of Deep Learning Development

The development of deep learning can be traced back to 1943, when McCulloch and Pitts proposed the artificial neuron model, laying the theoretical foundation for neural networks. In 1957, Rosenblatt invented the perceptron, the first algorithm capable of being trained through supervised learning. In the 1980s, Fukushima proposed the Neocognitron, an early prototype of convolutional neural networks, while Rumelhart and others popularized the backpropagation algorithm in 1986, making the training of multilayer neural networks possible. In 1997, Hochreiter and Schmidhuber proposed the LSTM model, addressing the long-term dependency problem in sequential data. In 2006, Hinton and his team introduced deep belief networks (DBN), marking the beginning of the deep learning renaissance. In 2012, AlexNet, developed by Krizhevsky and others, achieved breakthroughs in the ImageNet competition, demonstrating the powerful performance of deep convolutional networks. Subsequently, the introduction of new architectures such as GAN (2014), ResNet (2015), and Transformer (2017) further propelled the application of deep learning in fields like image and language processing. In recent years, with the emergence of the GPT series models (2018, 2020) and AlphaFold (2020), deep learning has achieved disruptive progress in natural language processing and biomedicine.

Table 2.4 Development History of Deep Learning

2.62 Basic Concepts and Methods of Deep Learning

Deep learning is a branch of machine learning characterized by the use of artificial neural networks with multiple hidden layers to achieve complex feature extraction and pattern recognition through end-to-end learning. Compared to traditional machine learning methods, deep learning has the following advantages: it can automatically learn hierarchical feature representations from massive raw data, avoiding manual feature engineering; it can approximate any complex function through deep nonlinear mappings, possessing strong expressive and generalization capabilities; the end-to-end learning paradigm allows for collaborative optimization between layers, minimizing human prior assumptions (LeCun et al., 2015; Schmidhuber, 2015). Deep learning has made breakthrough progress in fields such as image recognition, speech processing, and natural language understanding, and has shown great application potential in the biomedical field (Esteva et al., 2019).

Deep learning networks have different architectures and types, suitable for processing data of different modalities. Feedforward neural networks are the most basic type, mainly including fully connected networks and convolutional neural networks (Goodfellow et al., 2016). In fully connected networks, neurons are fully interconnected, making them suitable for processing fixed-length structured data. Convolutional neural networks utilize local connections, weight sharing, pooling, and other techniques, excelling at processing grid topology data such as images (Krizhevsky et al., 2012). Recurrent neural networks, such as long short-term memory networks, introduce a recurrent mechanism that can model long-range dependencies in sequential data (Hochreiter & Schmidhuber, 1997). Graph neural networks propagate information through defined operations like convolution and attention on the nodes and edges of graph structures, excelling at processing graph-structured data such as molecular graphs and knowledge graphs (Kipf & Welling, 2017). Generative adversarial networks learn through the game between a generator and a discriminator, capable of generating realistic data samples (Goodfellow et al., 2014). Additionally, autoencoders and restricted Boltzmann machines are commonly used unsupervised feature learning models (Hinton & Salakhutdinov, 2006). Different types of network structures can be flexibly combined to form more complex heterogeneous networks, such as combining convolutional and recurrent neural networks to process video data.

The training of deep learning models is an end-to-end optimization process. Taking supervised learning as an example, given labeled training data, a multi-layer neural network architecture is designed, a loss function suitable for the task is defined, and optimization algorithms such as backpropagation and stochastic gradient descent are used to minimize the loss function, updating the network parameters to obtain the optimal model (Rumelhart et al., 1986). The generalization performance of the model is evaluated using an independent test set. Hyperparameters such as network depth and width are tuned using a validation set. The success of deep learning is largely attributed to three factors: the accumulation of massive training data, advancements in large-scale parallel computing capabilities, and innovations in normalization techniques and network structures (LeCun et al., 2015; Schmidhuber, 2015). To prevent overfitting and improve generalization performance, methods such as dropout, L1/L2 regularization, and early stopping are widely used (Srivastava et al., 2014). Strategies like data augmentation and transfer learning are also very effective when data is scarce (Pan & Yang, 2010). For unlabeled data, unsupervised and self-supervised pre-training methods train feature extractors through auxiliary tasks such as reconstruction and contrastive learning, which can serve as a starting point for supervised learning in downstream tasks, accelerating convergence and improving performance (Chen et al., 2020). Overall, the efficient training and thorough tuning of deep learning models are key to ensuring their performance.

The powerful feature extraction and modeling capabilities of deep learning are the core advantages of deep models, but their "black box" nature limits their application in high-risk decision-making fields. In recent years, research on the interpretability of deep learning has received significant attention, and numerous methods have been proposed to explain the decision logic of deep models. These methods can be roughly divided into two categories: post-hoc explanation methods and interpretable models. Post-hoc explanation methods analyze the model after training is complete, such as saliency maps and gradient-based attribution methods that reveal input areas that significantly impact prediction results (Simonyan et al., 2013); perturbation-based methods assess the importance of a region by observing changes in output when part of the input is occluded (Zeiler & Fergus, 2014). Interpretable models consider interpretability factors during the model construction phase, such as attention mechanisms that reveal the relative importance of different inputs through attention weights (Bahdanau et al., 2015); modular networks decouple complex systems into interpretable modules; prototype networks characterize data by learning a set of interpretable prototypes, with new samples classified based on their similarity to the prototypes (Li et al., 2018). Additionally, methods such as knowledge distillation and rule extraction also help convert the knowledge learned by deep networks into forms that are interpretable to humans (Hinton et al., 2015). Although significant progress has been made in deep learning interpretability research, existing methods still face issues such as limited applicable scenarios and a lack of unified evaluation standards, necessitating systematic efforts from broader perspectives such as causal reasoning and cognitive science.

2.6.3Progress in the Application of Deep Learning in Bioinformatics

Deep learning is initiating a new revolution in bioinformatics research. Traditional machine learning methods are limited by the lack of effective features, while deep learning can directly learn hierarchical features end-to-end from raw biological data, greatly facilitating the extraction of value from biological big data. Deep learning models such as CNNs, RNNs, and GNNs are widely applied in various fields of biomedicine, demonstrating outstanding performance from the molecular and cellular levels to the tissue, organ, and individual levels, accelerating the progress of disease mechanisms and treatment research. This section summarizes the current research status and progress of deep learning in representative bioinformatics fields such as genomics, proteomics, and medical imaging.The specific applications of deep learning are shown in the table below:

Table 2.5 Functional Table of Deep Learning in Medical Imaging

In the field of genomics, deep learning is widely used for feature representation and functional prediction of nucleic acid sequences. CNNs can model local patterns in DNA sequences and have achieved significant results in tasks such as promoter recognition, chromatin accessibility prediction, and transcription factor binding site identification (Alipanahi et al., 2015). Compared to traditional methods based on manual features, CNNs can automatically extract discriminative sequence features and consider long-range regulatory patterns (Zeng et al., 2016). Recurrent neural networks, such as LSTM, excel at processing RNA sequences and are used for modeling RNA secondary structures and predicting interactions between RNA and RNA-binding proteins (Hochreiter & Schmidhuber, 1997; Maticzka et al., 2014). Graph neural networks are used to integrate multi-omics data from epigenomics by modeling regulatory relationships between genes, genes and enhancers, etc., to predict gene expression regulatory networks (Wang et al., 2021). In variant analysis and disease risk prediction, deep learning models such as variational autoencoders are used to integrate multi-dimensional omics data, including genetic variations, epigenetic modifications, and gene expression, to characterize the joint effects of variants and discover key genetic locus combinations associated with diseases, demonstrating performance that surpasses traditional GWAS methods (Cao et al., 2018).

In the fields of proteomics and structural biology, deep learning has been widely applied in protein sequence and structural feature learning, protein function prediction, molecular docking, and other areas (Senior et al., 2020). CNNs and RNNs have been used to model amino acid sequences, achieving significant performance improvements in predicting tasks such as protein secondary structure, disordered regions, and subcellular localization by learning evolutionary conserved features from large-scale protein sequence data, laying the foundation for subsequent structural modeling and functional research (Heffernan et al., 2017). Graph neural networks have been used to model the spatial proximity relationships of protein residues, achieving breakthroughs in tasks such as protein structure prediction and protein function prediction through end-to-end learning of three-dimensional structural features (Jumper et al., 2021). For membrane proteins and other structures that are difficult to obtain experimentally, deep learning-based de novo structure prediction methods provide new ideas for their functional research and drug design (Senior et al., 2020; Xu et al., 2021). In terms of protein interactions, deep learning models have been used to model the physicochemical properties of protein interfaces, predict complex structures, and reveal the intrinsic laws of interactions, providing important clues for elucidating cellular signaling pathways and metabolic regulatory networks (Gainza et al., 2020).

Medical imaging is the most mature and widely used field of deep learning in clinical applications. From cellular pathology slices to CT and MRI of organs and tissues, and to individual-level endoscopy and ultrasound imaging, deep learning is ushering in a new wave of imaging research. CNNs are widely used for tumor classification diagnosis and segmentation localization. By automatically learning morphological features from pathology slices, CNNs have achieved a level of interpretation comparable to that of pathologists in the histological grading of various cancers such as breast cancer and lung cancer, and are expected to become a valuable assistant in pathological diagnosis (Esteva et al., 2017). In radiological imaging, CNNs are used for intelligent analysis of images such as lung nodules, mammography, and brain MRI, achieving high-precision detection and classification of tumors through automatic texture feature extraction, greatly enhancing diagnostic efficiency (Lakhani & Sundaram, 2017). In endoscopic and ultrasound imaging, CNNs have also demonstrated significantly better performance than traditional methods, and are expected to become a new intelligent screening tool for gastrointestinal tumors (Zhang et al., 2021). Deep learning is also used to integrate imaging data and clinical information, achieving personalized treatment decisions guided by imaging through learning multimodal representations, showing broad application prospects in precision medicine (Nie et al., 2016).

Although deep learning has achieved remarkable results in the field of bioinformatics, its application still faces many challenges due to the particularity of biomedical big data. First, biological data generally have issues such as expensive labeling and varying quality, which makes it difficult to train models with good generalization. Methods for few-shot learning, such as active learning and unsupervised learning, are worth further exploration (Settles, 2012). Second, biological data often have high dimensionality, complex correlations, and dynamics, which impose higher requirements on the model's expressive power. Methods that integrate various prior knowledge, such as multi-scale representation learning and heterogeneous network modeling, are expected to further enhance the performance of deep models (Gilmer et al., 2017). In addition, most deep models make predictions in an end-to-end black-box manner, posing challenges for interpretation and transparency in clinical applications. Combining deep learning with interpretability methods such as causal reasoning and significance analysis is an important current research direction (Ribeiro et al., 2016). Finally, the application of deep learning in bioinformatics also requires guidance from specialized domain knowledge. How to integrate expert knowledge such as biological mechanisms and clinical guidelines into deep models to avoid false discoveries caused by blind fitting is an urgent problem to be solved (Yu et al., 2021).

2.7 Multi-task Learning and Transfer Learning

2.7.1 Basic Principles and Methods of Multi-task Learning

Multi-task learning is a machine learning paradigm that improves the model's generalization ability and learning efficiency by simultaneously learning multiple related tasks and leveraging the correlations between them. Compared to traditional single-task learning, the advantages of multi-task learning include: knowledge transfer between related tasks can alleviate the problem of sparse labeled data; complementary information between different tasks helps to learn more general feature representations; joint training avoids the storage overhead of multiple single-task models [150]. Depending on whether there is an explicit distinction between primary and secondary tasks, multi-task learning can be divided into symmetric and asymmetric types. Symmetric multi-task learning treats all tasks equally, aiming to enhance the overall performance of all tasks. Asymmetric multi-task learning explicitly defines primary and auxiliary tasks, using auxiliary tasks to improve the performance of the primary task. From the perspective of the three elements of machine learning, multi-task learning is mainly reflected in three aspects: model structure, loss function, and optimization algorithm.The framework of multi-task learning is shown in the figure below:

Figure 2.6 Transfer Learning Framework Diagram

At the model structure level, hard parameter sharing and soft parameter sharing are two mainstream architectures for achieving multi-task learning. Hard parameter sharing means that different tasks share part of the network parameters, and on this basis, task-specific output layers are added (Caruana, 1997). The shared layer is responsible for learning general representations between tasks, while the task-specific layer is used to extract personalized features for each task. Hard shared multi-task networks are easy to implement and have a high degree of sharing, but lack flexibility (Ruder, 2017). Soft parameter sharing allows different tasks to use different networks, but adds regularization constraints between the networks to encourage similar parameter distributions for different tasks, achieving soft coupling of parameters (Duong et al., 2015). Soft sharing gives tasks greater flexibility, but has a larger number of parameters and is more complex to implement. Some improvement methods, such as cross-convolutional networks, achieve feature interaction by adding cross-connections between tasks (Zhang et al., 2014), while gating mechanisms control the degree of interaction between different tasks through task gates (Liebel & Körner, 2018). These improved multi-task networks enhance knowledge transfer between tasks while also considering the modeling of personalized features for each task.

At the level of loss functions, the simplest strategy is to perform weighted summation of the loss functions for multiple tasks, with weights set a priori based on the importance and difficulty of the tasks, or optimized as hyperparameters (Sørensen et al., 2020). Some improved loss functions, such as uncertainty-weighted loss, parameterize task weights and adaptively learn through backpropagation (Kendall et al., 2018). Gradient normalization loss introduces a normalization term to balance the scale differences of gradients across different tasks (Chen et al., 2018). Adversarial multi-task loss distinguishes the features of different tasks through a discriminator network, encouraging the learning of task-invariant general feature representations (Liu et al., 2017). These improved loss functions help to consider the importance of different tasks and alleviate optimization conflicts between tasks. In terms of optimization algorithms, joint training and alternating training are two main strategies for optimizing multi-task networks (Caruana, 1997). Joint training mixes samples from all tasks within each batch, with each update affecting all tasks. Alternating training, on the other hand, trains different tasks in turn across different batches, with each update only affecting the current task (Zhang & Yang, 2017). Some adaptive adjustment strategies have been proposed to balance the optimization pace between tasks, such as dynamic task prioritization, which uses the task loss of the current epoch as the basis for allocating task samples in the next epoch (Sener & Koltun, 2018), and adaptive loss balancing, which dynamically adjusts the weights of each task based on historical gradients (Liu et al., 2019). These optimization strategies can accelerate model convergence and improve overall performance by dynamically balancing the learning progress of different tasks.

2.7.2 The Application of Transfer Learning in Ovarian Cancer Image Analysis

Transfer learning is a machine learning paradigm that utilizes previously learned knowledge to assist in learning new tasks that are different but related. The distinction from multi-task learning is that transfer learning primarily focuses on applying existing knowledge to new tasks rather than simultaneously optimizing multiple tasks. Based on the availability of labels in the source and target domains, transfer learning can be divided into various types such as supervised, semi-supervised, and unsupervised. From the perspective of machine learning models, transfer learning mainly reuses knowledge across different tasks through strategies such as feature representation transfer, model parameter transfer, and relationship mapping transfer (Pan & Yang, 2010). Deep neural networks, with their powerful representation learning capabilities, have injected new vitality into transfer learning. By pre-training on large-scale datasets and fine-tuning on target tasks, deep transfer learning models can significantly alleviate overfitting issues in small sample learning, accelerating model convergence and generalization (Yosinski et al., 2014). Deep transfer learning has achieved widespread success in fields such as computer vision and natural language processing, and is rapidly penetrating the field of medical imaging (Shin et al., 2016).

In the field of ovarian cancer imaging analysis, the cost of obtaining large-scale labeled data from medical imaging devices is extremely high, making transfer learning a promising key technology to break through the bottleneck of data scarcity. In tumor detection tasks, pre-training deep learning models on large datasets of natural images to learn general visual feature extractors, and then transferring them to medical images such as ovarian cancer CT and MRI, can significantly enhance the model's ability to recognize tumor regions, alleviating the dilemma of scarce medical image labeling (Litjens et al., 2017). In the classification of benign and malignant tumors, transfer learning is used to improve classification generalization performance under small sample conditions. For example, by pre-training classification networks like ResNet and DenseNet on ImageNet to learn robust multi-scale image features, and then fine-tuning them on ultrasound and CT images of ovarian tumors, the sensitivity and specificity of the classification model can be greatly improved (Zhou et al., 2019). For lesion segmentation tasks, transfer learning is used to alleviate the problem of scarce fine annotations. After pre-training segmentation networks like UNet and DeepLab on natural image datasets, fine-tuning them on MRI and PET images of ovarian tumors can accurately outline lesion contours even under conditions of limited pixel-level annotations, demonstrating the superiority of cross-domain transfer (Ronneberger et al., 2015; Kamnitsas et al., 2017).

The transfer from imaging modalities to types of diseases is another important issue in image analysis. Due to differences in imaging principles and imaging targets of different medical imaging devices, there are significant differences in resolution, contrast, noise levels, and other aspects among different modality images. At the same time, the anatomical structures and pathological features of different organs and tissues vary greatly, leading to significant differences in the imaging manifestations of different diseases. In this regard, transfer methods such as domain adaptation and few-shot learning have received much attention. By finding invariant feature representations across different imaging domains through adversarial learning, metric learning, and other methods, the performance of cross-modal transfer can be significantly improved. For example, a tumor segmentation model trained on CT images can be directly applied to MRI, achieving segmentation accuracy comparable to that of a model trained from scratch (Dou et al., 2018). By adopting meta-learning strategies and learning task-invariant feature extractors from multiple related disease small sample sets, rapid generalization of imaging models for rare diseases like ovarian cancer can also be achieved (Finn et al., 2017). In addition, transferring imaging knowledge from common diseases such as prostate cancer and lung cancer to ovarian cancer can help alleviate the sample imbalance problem, but the distribution shift caused by organ and disease specificity still needs to be approached with caution (Cheplygina et al., 2019).

Although transfer learning has begun to show its potential in the field of medical imaging, how to address issues such as the heterogeneity of multimodal data, the scarcity of fine annotations, and to improve the interpretability and generalization of models, while integrating prior knowledge such as anatomical structures and radiomics into the transfer framework, remains a pressing challenge. In the area of heterogeneous data transfer, it is necessary to design intelligent feature selection and matching strategies to adaptively align the source and target domains, and to introduce attention mechanisms to dynamically adjust the feature weights of different domains, in order to enhance the robustness of cross-modal transfer (Zhang et al., 2018). To address the issue of scarce annotations, active learning can be used to select a small number of samples that contribute the most to model improvement for labeling, while meta-learning can quickly adapt to new tasks from few samples by learning the commonalities between tasks. The combination of these two approaches is expected to further enhance the performance of few-sample transfer (Settles, 2009; Ren et al., 2018). Combining interpretable machine learning methods such as saliency analysis and rule extraction with deep transfer models is expected to achieve key area visualization and discriminative rule extraction, thereby enhancing the transparency of the clinical decision-making process (Selvaraju et al., 2017). Encoding domain knowledge bases such as anatomical structure maps and radiomics knowledge bases into the transfer learning framework can guide the model to learn feature representations that comply with prior knowledge constraints while also being data-driven, thus improving the rationality of knowledge transfer while enhancing performance (Kamnitsas et al., 2017).

Development and design of an AI-assisted ovarian cancer diagnosis platform based on deep learning framework.

2.8.1 Reasons for platform development

Ovarian cancer is considered one of the deadliest cancers in the female reproductive system, with early symptoms being subtle, leading most patients to be diagnosed at an advanced stage (III-IV), which severely affects treatment outcomes and patient survival rates (Torre et al., 2018). Traditional diagnostic methods for ovarian cancer mainly include imaging examinations, serological marker detection, and histopathological diagnosis. Imaging examinations (such as ultrasound, CT, and MRI) can provide macroscopic information about tumors, but they lack sufficient resolution for early lesions (Nassir et al., 2020). Serological markers such as CA-125 and HE4 have poor sensitivity and specificity in early detection, leading to false positives or false negatives (Buys et al., 2011). Although histopathological examination is the gold standard for diagnosis, it is invasive, time-consuming, and highly dependent on the technician's skills (Prat, 2014). Furthermore, ovarian cancer has high heterogeneity, making it difficult for traditional single methods to accurately analyze its complex pathological features, which severely limits the implementation of precise diagnosis and personalized treatment strategies (Matulonis et al., 2016).

2.8.2 Advantages of Developing Deep Learning and Computer Vision-Assisted Diagnosis Platforms

The rapid development of deep learning technology in the fields of medical image analysis and computer vision has brought new opportunities for the precise diagnosis of ovarian cancer (Litjens et al., 2017). Deep learning algorithms can automatically extract potential features through training on a large amount of medical imaging data, enhancing the sensitivity and consistency of diagnoses (Esteva et al., 2019). On one hand, deep learning models have a high degree of automation, significantly reducing doctors' subjective biases and human errors (Ribli et al., 2018). On the other hand, this technology can capture subtle lesions that are difficult to detect with traditional methods, showing excellent performance especially in early ovarian cancer screening (Shen et al., 2017). The fusion of multimodal data further broadens the application scope of deep learning, and by combining imaging, genomic, and clinical text information, it can comprehensively improve diagnostic accuracy (Huang et al., 2020). In addition, deep learning models can dynamically predict disease progression, assisting doctors in formulating more precise treatment plans (Yala et al., 2019).

Principles and Necessity of Designing an Integrated Diagnosis Platform Based on Image Modality and Text Modality

The complexity of ovarian cancer determines that single-modal data is insufficient to support comprehensive and accurate diagnosis (Zhang et al., 2019). Imaging data (such as MRI, CT, etc.) can visually display tumor morphology but cannot reflect microscopic biological characteristics such as gene mutations and patient history (Zhou et al., 2019). Text data (such as electronic medical records and genetic testing reports) record rich clinical information but lack spatial structure (Wang et al., 2019). Therefore, it is crucial to design a comprehensive diagnostic platform that deeply integrates imaging and text modalities. This platform can map different data features to the same representation space through multimodal deep learning models, achieving feature complementarity and collaborative analysis (Xu et al., 2020). By fully exploring the potential associations between different data types, it can not only improve the accuracy and sensitivity of ovarian cancer diagnosis but also provide a scientific basis for developing personalized treatment plans (Liang et al., 2020).

2.8.4 Key Modules and Architecture Design of the Integrated Diagnosis Platform and User Usage

The comprehensive diagnostic platform mainly includes five core modules: data input, data preprocessing, multimodal fusion, diagnostic decision-making, and result output (Chen et al., 2020). In order to achieve precise diagnosis and treatment support for ovarian cancer, these modules need to operate efficiently in coordination, forming a complete intelligent diagnostic process,as shown in the diagram below:

Figure 2.7 Platform Development Schematic

Data input module

The data input module is the foundation of the platform, responsible for the collection and integration of multi-source data. The platform receives data from different devices and channels, including medical imaging data (MRI, CT, PET, etc.), pathological slice images, serum biomarker test results, electronic health records (EHR), genomic sequencing data, and text information such as patient history. To ensure data quality and consistency, the input data must undergo standardization processes, such as converting imaging data to DICOM format, normalizing text data formats, and filling in missing data (Rieke et al., 2020). In addition, to ensure data security and privacy protection, the platform employs encryption storage and access control mechanisms, adhering to medical data protection regulations (such as HIPAA and GDPR) to prevent the leakage of sensitive patient information.

Data preprocessing module

The preprocessing module is a preliminary step in data analysis, aimed at improving the accuracy and stability of subsequent model analysis. For image data, advanced deep learning models (such as U-Net, DenseNet) are used for noise reduction, tumor region segmentation, and feature extraction (Ronneberger et al., 2015; Huang et al., 2017). These models can accurately segment tumor regions and extract multidimensional features such as morphology, texture, and boundaries, reducing the errors in manual annotations by doctors.

For text data, the platform utilizes natural language processing (NLP) technologies (such as BERT, GPT) to extract information from electronic medical records, genetic testing reports, and laboratory test results (Vaswani et al., 2017). NLP models can automatically identify and extract key information, such as patients' medical history, family genetic history, laboratory indicators, etc., providing rich semantic feature support for multimodal data fusion. In addition, the platform also employs techniques such as text normalization, entity recognition, and relationship extraction to enhance the usability and information depth of text data.

Multimodal fusion module

Multimodal data fusion is one of the core technologies of the platform. Traditional unimodal data is difficult to comprehensively characterize the complex pathological features of ovarian cancer, while multimodal data fusion can integrate imaging and textual information to achieve comprehensive and multi-level diagnostic analysis (Zhou et al., 2021).

Ourplatformplansto adopt cross-modal alignment algorithms (such as Transformer, multimodal attention mechanisms, etc.) for deep integration of different data modalities (Tsai et al., 2019). For example, the image modality extracts spatial features through convolutional neural networks (CNN), while the text modality utilizes NLP models to extract semantic features. Subsequently, the two types of features are mapped to the same feature space through a cross-modal alignment network (such as Multimodal Transformer), achieving efficient fusion at the feature level. This fusion method not only enhances the accuracy of diagnosis but also effectively uncovers potential correlations between imaging and text data.

In addition, the platform we designed plans to adopt a multi-task learning strategy (MTL), completing tasks such as tumor typing, staging, risk assessment, and treatment response prediction simultaneously within the same model, enhancing the model's generalization ability and robustness (Ruder, 2017).

Diagnostic Decision Module

In the diagnostic decision-making module, the platform uses multimodal fusion data and employs ensemble learning and deep learning models for the classification, staging, and risk assessment of ovarian cancer. For example, classification models (such as ResNet, XGBoost) are used to predict tumor types, while regression models (such as random forest regression) are used for quantitative analysis of patient survival risk (He et al., 2020).

The platform also integrates survival analysis-based models (such as the Cox proportional hazards model and deep learning survival models) to assess patients' prognostic risks and disease progression trends. In addition, based on the concept of personalized medicine, the platform combines patients' genetic backgrounds, medical histories, and lifestyles to provide targeted treatment recommendations for clinicians (Johnson et al., 2020).

Result output module

The result output module is the interactive interface between the platform and clinical doctors and patients, designed to present complex diagnostic results in an intuitive and understandable way. The platform uses data visualization technology (such as interactive charts and 3D visualization technology) to display tumor detection results, classification and staging information, and treatment recommendations (Fan et al., 2020). Doctors can view the distribution of tumor areas, trends in size changes, and risk scores through a graphical interface, and adjust treatment plans accordingly.

In addition, our platform also plans to support the generation of personalized diagnostic reports, which will detail the basis for diagnosis, model explanations (such as heat maps and feature weights), and treatment recommendations, helping doctors understand the model's decision-making process, enhancing the model's interpretability and clinical trust.

To improve user experience, the platform provides multi-end access support (such as web, mobile, and hospital information system integration), ensuring that doctors and patients can conveniently access diagnostic results and health management recommendations. This comprehensive diagnostic platform achieves a comprehensive, precise, and intelligent diagnosis of ovarian cancer through efficient data input and preprocessing, advanced multimodal data fusion, accurate diagnostic decision-making, and intuitive result output, providing strong support for clinical decision-making and personalized treatment (Chen et al., 2020; He et al., 2020; Tsai et al., 2019).

2.8.5 The Application Value and Prospects of Multimodal Diagnostic Platforms

The comprehensive diagnostic platform based on multimodal deep learning has important value for the early detection, disease monitoring, and personalized treatment of ovarian cancer (Gao et al., 2020). The fusion of imaging and text data significantly improves the sensitivity and specificity of diagnosis, especially excelling in the identification of early lesions and dynamic monitoring (Zhang et al., 2021). In the future, this platform can be further expanded to the diagnosis and treatment of other tumor types such as breast cancer and lung cancer (Wang et al., 2021). In addition, by combining real-time big data updates with cloud computing technology, the platform will achieve more efficient data processing and model iteration (Shen et al., 2020). With the continuous advancement of deep learning technology and medical data, this platform will play a greater role in promoting precision medicine and intelligent diagnosis.

2.9Chapter Summary

This literature review discusses in detail various aspects of ovarian cancer, from its definition, epidemiological characteristics, advancements in imaging diagnostic techniques, to the application of deep learning technologies and the design and development of comprehensive diagnostic platforms, aiming to present the latest progress and future directions in the field of ovarian cancer research. Ovarian cancer, as one of the deadliest malignant tumors in the female reproductive system, poses significant challenges for early diagnosis and has a poor prognosis, resulting in most patients being diagnosed at an advanced stage, which severely affects treatment outcomes and survival rates. Traditional diagnostic methods such as imaging examinations (ultrasound, CT, MRI) and serum marker tests (such as CA-125 and HE4) are widely used in clinical practice, but they have significant shortcomings in sensitivity and specificity, especially performing poorly in early detection. Furthermore, although histopathological diagnosis is considered the gold standard for confirmation, its invasiveness, time-consuming nature, and reliance on the experience of technicians limit its widespread application. In recent years, medical imaging technology has made breakthrough advancements, evolving from initial X-rays to today's multimodal imaging technologies, including PET-CT and MRI, as well as deep learning technologies that combine radiomics and artificial intelligence, greatly enhancing the accuracy and efficiency of ovarian cancer detection and diagnosis. Among these, convolutional neural networks (CNN) and attention mechanism-based deep learning models have demonstrated outstanding performance in image segmentation, classification, and disease prognosis prediction, effectively addressing the limitations of traditionally manually designed features by automatically extracting high-dimensional features. More importantly, the introduction of multimodal data fusion technology has made it possible to conduct collaborative analysis between imaging data and text data (such as electronic medical records and genetic testing reports), which not only improves the comprehensiveness and accuracy of diagnoses but also provides a scientific basis for formulating personalized treatment plans. Additionally,
We also
This paper elaborates on the design of an artificial intelligence-assisted diagnostic platform based on a deep learning framework, which includes five core modules: data input, preprocessing, multimodal fusion, diagnostic decision-making, and result output. By integrating medical imaging and text data, it provides new technical support for the precise diagnosis and personalized treatment of ovarian cancer.
We envisioned the design of
The platform uses advanced deep learning models (such as U-Net, DenseNet) for lesion segmentation and feature extraction from image data, while utilizing natural language processing technologies (such as BERT, GPT) to extract key clinical information from text data, achieving deep data integration through cross-modal alignment algorithms. In addition, the platform also integrates survival analysis models for disease classification, staging, and risk assessment, and combines data visualization techniques to provide intuitive diagnostic results and personalized recommendations for doctors and patients.
Although
Significant progress has been made in the field of ovarian cancer diagnosis using deep learning technology, but existing technologies still face many challenges, such as the complexity of data integration, insufficient model interpretability, and optimization issues in multimodal data fusion algorithms. Therefore, future research needs to further strengthen efforts in deep learning algorithm design, multimodal data processing, and clinical translation, exploring how to apply these technologies more broadly in precision medicine to improve early detection rates for ovarian cancer patients, enhance treatment outcomes, and promote the widespread application of intelligent diagnostic technologies in other types of cancer. The development of AI-assisted diagnostic technologies and platforms based on deep learning is not only an important direction for ovarian cancer research but also provides a new technological paradigm for the field of precision medicine, bringing unprecedented opportunities and challenges in tackling the complex disease of ovarian cancer.

Chapter 3RESEARCH METHODOLOGY

3.1 Overall Research Approach and Technical Plan

3.1.1 Technical Route Design

This study aims to explore key scientific issues in the imaging analysis and intelligent diagnosis of ovarian cancer outlined in Chapter Two, focusing on different aspects such as image feature representation, tumor heterogeneity characterization, and intelligent diagnostic decision-making, particularly through methods driven by artificial intelligence and deep learning.This study intends to follow a technical route of "data-driven - model innovation - system development - application optimization" to conduct systematic methodological exploration and engineering practice in the field of ovarian cancer imaging analysis and intelligent diagnosis. The specific technical roadmap is as follows:

Figure 3.1 Technical Roadmap

The research stages are as follows:

(1) Data aggregation and quality control phase: Widely collect multi-center, multi-modal medical imaging data related to ovarian cancer, including CT, MRI, PET, etc., as well as corresponding clinical, pathological, and genomic data. Perform quality control on the imaging data, removing sequences with artifacts and severe motion contamination. For sequences with missing metadata, manually register and complete key information. Also, collect a certain amount of normal control group images. Using software like ITK-SNAP, under the guidance of cancer imaging experts, finely annotate the tumor ROI to form a high-quality dataset.

(2) Radiomics feature extraction phase: For different modalities of imaging such as CT, MRI, and PET, traditional multi-scale texture, shape, and gray level statistics radiomics features are used, as well as end-to-end feature extraction methods based on deep learning. Traditional handcrafted features can be calculated using open-source toolkits like PyRadiomics. Deep learning feature extraction employs multi-scale, multi-channel end-to-end convolutional neural networks, using tumor ROI images as input, optimizing network training through strategies like transfer learning, with the final layer of convolutional feature maps representing the learned deep radiomics features.

(3) Tumor heterogeneity characterization modeling stage: Based on the extracted radiomic features, model and analyze the imaging heterogeneity of ovarian cancer tumors. Extract local patch features from each tumor ROI, and use cluster analysis to identify sub-regions within the tumor; validate the correspondence between clustering results and histopathological types using postoperative pathological slices. At the inter-tumor level, discover tumor subgroups through methods such as non-negative matrix factorization, and compare them with molecular typing such as gene expression and mutations to validate the biological significance of radiomic subtypes. Further, use survival analysis and other methods to reveal the prognostic differences among the subtypes.

(4) Intelligent diagnosis and prognosis prediction stage: Based on key radiomic features discovered through heterogeneity modeling, construct an intelligent diagnosis and prognosis prediction model for ovarian cancer. Use ensemble learning algorithms such as AdaBoost and LightGBM to model handcrafted imaging features, and employ a CNN model optimized by transfer learning to learn end-to-end diagnostic feature representation. On this basis, classify benign and malignant tumors, evaluating model performance against postoperative pathological diagnosis as the gold standard. At the same time, integrate radiomic features with clinical indicators, using machine learning methods such as Cox proportional hazards regression and Lasso regularization to construct recurrence and survival risk prediction models.

(5) Knowledge-guided small sample learning phase: In response to the issue of limited sample sizes for rare pathological subtypes, research integrates prior knowledge into small sample learning strategies. Utilizing ImageNet pre-trained models to transfer low-level general features, aiming to better model isolated small samples. Conducting multi-view joint embedding of multimodal data such as imaging, clinical, and genomic data to complement different modal signals. At the same time, organizing imaging anatomical structures and radiological histology knowledge in a graph format to guide model design and constrain the training process. Through active learning methods and interaction with experts, selecting the most representative samples for priority labeling, reducing reliance on large-scale labeled data.

(6) Development stage of the clinical auxiliary diagnosis system: Based on the above technical solutions, an artificial intelligence model and knowledge base are formed to develop a clinical auxiliary diagnosis system for ovarian cancer that integrates imaging omics analysis, diagnostic decision support, and prognostic prediction assessment. The system includes modules for medical imaging data management, feature extraction and analysis, visualization display, and diagnostic suggestion generation. The system backend is built using web frameworks such as Flask and Django to achieve modularization of computing services; the system frontend is developed using frameworks like Vue and React to design a smooth and aesthetically pleasing human-computer interaction interface. Pilot applications of the system are carried out in cooperating hospitals, and multidisciplinary experts are involved for continuous feedback and optimization of the system's functions and processes.

This research focuses on the field of imaging genomics analysis and intelligent diagnosis of ovarian cancer, with deep learning and transfer learning as the core. Through innovative methods such as end-to-end image feature representation, tumor heterogeneity modeling, small sample learning, and knowledge-guided modeling, it aims to form a comprehensive AI technology solution from image features to intelligent diagnosis. The goal is to achieve breakthroughs in key technical bottlenecks in ovarian cancer imaging genomics research, accelerate the application of artificial intelligence in complex tumor intelligent diagnosis and treatment, and provide new ideas, methods, and tools for precision medicine in ovarian cancer.

3.1.2 Analysis of Key Scientific Issues

The AI diagnosis of ovarian cancer imaging faces numerous challenges, including the diversity of tumor imaging manifestations, complex heterogeneity, a lack of samples for rare subtypes, and insufficient integration of experiential knowledge. It involves a series of key scientific issues related to imaging feature representation, characterization of tumor heterogeneity, knowledge-guided modeling, and implementation of clinical applications. In response to these challenges, this study comprehensively employs cutting-edge theoretical methods from interdisciplinary fields such as artificial intelligence, radiomics, and computer vision, aiming to achieve breakthrough progress in theoretical innovation and key technological advancements. The main key scientific issues are analyzed as follows:

(1) How to learn end-to-end the feature representations related to diagnostic decisions contained in imaging data

Traditional handcrafted imaging features are sensitive to noise, have insufficient generalization performance, and rely on the prior experience of radiologists for feature design, making it difficult to model high-order semantic information. End-to-end deep learning methods provide a new breakthrough for imaging feature representation learning. This study aims to explore the design of a multi-scale convolutional neural network for ovarian cancer diagnosis tasks, adaptively learning hierarchical features from local to global and from shallow to deep layers. The network is pre-trained on large-scale datasets of natural images like ImageNet to acquire general visual feature patterns, and then fine-tuned on ovarian cancer datasets to achieve transfer learning of specific disease semantic features (Krizhevsky et al., 2012; Litjens et al., 2017). In addition, introducing attention mechanisms allows the network to focus on features in the lesion area, which can further enhance classification and prediction performance (Vaswani et al., 2017; Zhang et al., 2021).

(2) How to effectively characterize the imaging heterogeneity features within and between ovarian cancer tumors

Ovarian cancer tumors exhibit significant morphological and structural heterogeneity, which is closely related to genetic molecular mechanisms and prognostic risks. However, traditional radiomics analysis often extracts global average features, neglecting the differences in local tumor regions. This study aims to use non-negative matrix factorization, clustering, and other methods to discover and characterize the imaging heterogeneity of ovarian cancer (Gillies et al., 2016). Within the tumor, the tumor ROI is divided into multiple local patches to extract local texture and shape features, and clustering analysis is used to identify different heterogeneous regions within the tumor; between tumors, clustering is performed on the tumor ROIs of multiple cases based on global features to identify different subtypes (Aerts et al., 2014). Further survival analysis is conducted to explore the prognostic differences of each imaging subtype, and genomic data is utilized to investigate their intrinsic molecular mechanisms (Zhu et al., 2017; Lambin et al., 2017). This is of great significance for understanding the biological behavior of ovarian cancer and guiding treatment.

(3) How to combine prior knowledge and machine learning methods to achieve robust learning under small sample conditions

Deep learning models typically require a large amount of training data, but the sample size for rare subtypes of ovarian cancer is very limited. At the same time, models trained under small sample conditions have poor generalization and are prone to overfitting. Combining prior knowledge from medical imaging with data-driven learning methods for robust modeling under small samples is a significant challenge. This study aims to explore a knowledge-guided transfer learning paradigm: first, using large-scale datasets of natural images to pre-train models and acquire general features; based on this, fine-tuning the model using prior knowledge such as anatomical structures and radiological histology to constrain its feature space (Pan & Yang, 2010; Shin et al., 2016). Multi-view projections of multimodal data will be performed to achieve the integration of complementary information. In addition, active learning methods can guide experts to prioritize labeling influential samples that contribute the most to model improvement, reducing the need for a large amount of labeled data (Settles, 2012; Ren et al., 2018). The comprehensive application of strategies such as transfer learning, active learning, and knowledge fusion is expected to significantly enhance the performance of intelligent analysis of ovarian cancer imaging under small sample conditions.

(4) How to organically integrate artificial intelligence technology with clinical diagnosis and treatment practices to develop practical auxiliary diagnostic systems

Currently, the application of imaging AI in clinical practice is still immature, and doctors lack trust in the diagnostic suggestions of black box models. How to seamlessly integrate AI methods with clinical diagnosis and treatment processes, endow models with interpretability, and develop auxiliary diagnostic systems that meet clinical practical needs is a major challenge. This study intends to use methods such as causal learning and knowledge graphs to reveal the causal relationships between imaging features and clinical diagnoses (Pearl et al., 2018). It aims to enable AI models to simulate the diagnostic reasoning process of radiologists, enhancing their interpretability and credibility (Selvaraju et al., 2017). At the same time, it actively collaborates with clinical experts to package AI models into streamlined, modular diagnostic services, forming human-machine collaboration with doctors to assist them in making more accurate and efficient diagnostic decisions (Topol, 2019). The system achieves rapid absorption of expert feedback and online model updates, continuously improving its performance. It develops a remote diagnostic platform aimed at grassroots hospitals, promoting the sinking of high-quality medical resources, allowing more patients to benefit (Rieke et al., 2020; He et al., 2020).

Thisresearch focuses on the core scientific issues of intelligent diagnosis of ovarian cancer imaging, using deep learning, knowledge-guided modeling, and causal reasoning as breakthroughs, striving to achieve original results in areas such as medical imaging feature representation, tumor heterogeneity characterization, robust learning with small samples, and the development of intelligent auxiliary diagnostic systems. This not only holds the promise of innovation in the theory, methods, and systems of ovarian cancer imaging analysis but also opens new pathways for empowering medical imaging with artificial intelligence and assisting in precise tumor diagnosis and treatment, which is of great significance for serving the healthworldstrategy and improving the level of cancer prevention and treatment (Litjens et al., 2017; Gillies et al., 2016; Topol, 2019).

3.2 Research Time Dimension

Cross-sectional research, also known as transverse research or cross-sectional study, involves collecting relevant data around a research topic within a specific and relatively short period to describe the characteristics of the research subject at that point in time or to explore the relationships between variables. In this context, "specific time period" does not refer to an exact moment but rather to a short and controllable time range, such as a few weeks, a few months, or a specific developmental stage. In contrast, longitudinal research (also known as tracking research) systematically observes and studies changes or developments in a phenomenon over a longer period, typically aiming to track trends, developmental processes, or changes in research outcomes (Cross-Sectional Research and Longitudinal Research, n.d.).

Given the long time cycle and high implementation difficulty required for longitudinal studies, this research adopts a cross-sectional time dimension for analysis. Within a specific and relatively short time frame, it studies the user experience and diagnostic effectiveness of the AI-assisted ovarian cancer diagnosis platform in clinical applications. By focusing on a defined time period, the aim is to capture the current state of how the platform integrates multimodal data and supports the decision-making process in a medical environment, providing data support and theoretical basis for further optimization of the diagnostic platform.

3.3Dataset Construction and Preprocessing

3.3.1 Data Sources and Collection Methods

A high-quality medical imaging dataset is the foundation for imaging AI analysis. According to the goals and content of this study, we plan to gather multi-center, multi-modal big data on ovarian cancer imaging, covering imaging data such as CT, MRI, and PET, pathological diagnosis information, genetic testing information, as well as demographic, clinical treatment, and follow-up prognosis data, striving to comprehensively reflect the imaging, clinical, and biological aspects of the occurrence and development of ovarian cancer. The sources and collection methods of the data mainly include: hospital clinical data collectionfor validation. This study willestablisha data-sharing collaborative relationship with several top-tier hospitals' gynecology, radiology, and pathology departments. Clinical doctors will select eligible ovarian cancer patients based on inclusion and exclusion criteria and fill out a standardized clinical questionnaire to collect patients' demographic information, medical history, surgery, medication, follow-up data, etc. A dedicated person will be responsible for collecting patients' CT, MRI, and PET imaging data, strictly standardizing the scanning instruments and imaging parameters to ensure image quality; at the same time, pathological diagnosis and staging information will be collected.

Genomic data collection. For ovarian cancer patients who have undergone genetic testing, apply to the original testing institution for whole genome sequencing, copy number variation chip data, etc.; for patients with sufficient specimen volume, collect fresh tissue specimens and entrust qualified third-party institutions to conduct whole exome sequencing, RNA sequencing, and other molecular tests. Summarize mutation, copy number variation, expression profile, and other omics data, and integrate them with imaging and clinical information.

Public database mining. Comprehensive retrieval of public medical imaging and omics databases such as TCIA, TCGA, and GEO, downloading datasets related to ovarian cancer. Among them, TCIA has a wealth of ovarian cancer imaging data, including CT, MRI, and PET imaging, as well as clinical information; TCGA and GEO contain corresponding molecular omics data such as gene expression, mutations, and copy number. An automated crawler program is used for data collection, and metadata is manually organized to form a structured dataset.

Literature data extraction. Search for literature on ovarian cancer radiomics research in literature databases such as PubMed and Google Scholar, and extract reported features from the text. Literature data extraction. Search for literature on ovarian cancer radiomics research in literature databases such as PubMed and Google Scholar, and extract information such as reported feature sets and experimental results. For literature with missing original data, attempt to contact the authors for retrieval. Use natural language processing technology to extract information from the text, organizing unstructured literature knowledge into a structured radiomics feature database.

Collaborative project efforts. Actively apply for national natural science funds, national key research and development programs, and other levels of research projects to seek special support. Through project execution, collaborate with domestic and international universities and research institutions for data sharing. Introduce ovarian cancer imaging data and clinical information collected by other research groups as a cross-validation set, and moderately share the data collected in this project to form a virtuous cycle of data sharing. In summary, this research will comprehensively collect ovarian cancer imaging, clinical, and genomic data from multiple dimensions, including hospital clinical pathways, genetic testing channels, public databases, literature mining, and collaborative projects. Under the premise of informed consent and ethical review, strive to gather a high-quality, multi-modal, and temporally and spatially extensive dataset, laying a solid data foundation for subsequent radiomics analysis and artificial intelligence modeling. It will also promote data sharing and academic exchange in this field.

3.3.2 Data Annotation, Quality Control, and Preprocessing

The standardization and normalization of multi-source heterogeneous medical data is key to dataset construction. The raw collected medical images, clinical data, and other data often have issues such as incompleteness, noise pollution, and inconsistent variables. Systematic labeling, quality control, and preprocessing need to be carried out under the guidance of professionals to ensure the usability of the data. The main technical route is as follows:

(1) Medical imaging data annotation. Using medical imaging annotation software such as 3D Slicer and ITK-SNAP, a trained team of graduate students, under the guidance of cancer imaging genomics experts, meticulously outlines tumor lesions. Multiple annotations and cross-validation methods are employed to measure the accuracy and consistency of the annotations using metrics such as the average Dice coefficient and Hausdorff distance. For cases with postoperative pathology, the imaging annotations are verified and corrected against the pathological gold standard.

(2) Structured clinical information. Conduct double data entry verification on the questionnaires filled out by clinical doctors, eliminating errors and inconsistent entries. For unstructured text information such as medical history, symptoms, and medication, use natural language processing techniques for entity recognition and semantic analysis to extract structured features. Additionally, standardize the coding of variables such as TNM staging and FIGO grading with reference to ovarian cancer diagnosis and treatment guidelines.

(3) Quality control of genomic data. Software such as FastQC is used to assess the quality of sequencing data, and low-quality reads are filtered using tools like Trimmomatic. Variants are detected using GATK, VarScan, etc., to obtain reliable somatic and germline mutation data. Some variant sites are verified through manual review and Sanger sequencing to ensure the accuracy of variant annotation. RNA-seq data is quantified for gene expression levels using Cufflinks, DEseq2, etc., and batch effect correction is performed.

(4) Medical imaging data preprocessing. For sequences with severe noise pollution, image denoising algorithms such as BM3D and WNNM are used for processing. For sequences with significant motion artifacts, deep learning-based MRI motion correction techniques are employed to restore high-quality images. At the same time, N4 bias correction is used to correct the issue of inhomogeneous field strength. Standardized spatial registration is performed to unify the image sequences to the MNI152 coordinate system.

(5) Multimodal data semantic mapping. According to patient ID, visit number, and other main indexes, spatial and semantic matching of medical imaging, clinical pathology, genomics, and other data is performed. Referencing ontologies and vocabularies such as RadLex, ICD, and SNOMED CT, an imaging genomics ontology is constructed to align imaging features with clinical diagnoses. Further integration of drug and pathway databases is carried out to build a comprehensive ovarian cancer knowledge base that incorporates imaging, clinical, genetic, and drug data. Based on the knowledge base, semantic links between multimodal features are established to lay the foundation for subsequent joint analysis.

3.4 This study intends to construct a questionnaire survey to collect early symptom data from ovarian cancer patients

The questionnaire survey is the main tool of this research, aimed at collecting early symptom data from ovarian cancer patients to analyze and identify patterns for improving early diagnosis and treatment strategies. The logical structure of the questionnaire is as follows:

Table 3.1: Questionnaire Structure

Section A: Patient Information

This section asks for identifying information about the patient, including basic demographic and medical background information. Questions cover:

Age, gender, marital status, and educational background.

Medical history, including chronic conditions or reproductive health issues (e.g., PCOS, endometriosis, or family history of ovarian cancer).

References: (Siegel et al., 2020; Torre et al., 2018)

Section B: Early Symptoms

This section includes questions aimed at identifying early symptoms experienced by ovarian cancer patients. It focuses on:

Physical symptoms such as abdominal bloating, pelvic discomfort, or appetite loss.

Menstrual irregularities or postmenopausal bleeding.

Duration and progression of these symptoms before seeking medical attention.

Respondents will rate the severity and frequency of symptoms using a 5-point Likert scale (1 = not at all, 2 = rarely, 3 = sometimes, 4 = often, 5 = always).

References: (Jelovac & Armstrong, 2011; Lheureux et al., 2019)

Section C: Diagnosis and Treatment History

This section explores the diagnostic journey and treatment interventions. Questions cover:

Time taken from symptom onset to diagnosis.

Types of diagnostic methods utilized (e.g., ultrasound, CT, MRI, or blood tests for CA-125).

Initial treatment plans, including surgery or chemotherapy.

This section aims to understand delays in diagnosis and barriers to effective treatment.

References: (Kurman & Shih, 2016; Lindemann et al., 2017)

Section D: Psychological and Lifestyle Impact

This section investigates the psychological and lifestyle impact of ovarian cancer, focusing on:

Emotional health and quality of life since the onset of symptoms.

The burden on daily activities and relationships.

Use of psychological support services or counseling.

Respondents will rate their emotional and lifestyle changes on a 5-point Likert scale, with space for open-ended comments.

References: (Moore et al., 2018; Bray et al., 2012)

Section E: Feedback and Suggestions

This section invites respondents to provide feedback on their diagnostic and treatment journey and suggestions for improving early detection and care. Questions include:

Factors that could have encouraged earlier diagnosis.

Recommendations for improving patient support services.

Views on awareness campaigns or educational resources for ovarian cancer.

References: (GLOBOCAN, 2020; Torre et al., 2018)

3.5 Analysis Unit

Survey

The study population includes ovarian cancer patients who have been diagnosed within the past five years. Participants will be recruited through hospitals, cancer support organizations, and online patient networks. The survey uses both offline and online modes to ensure broader geographical representation and accessibility.

Interview

Semi-structured interviews will be conducted with oncology specialists, gynecologists, and nurses who have experience treating ovarian cancer patients. These interviews will focus on the challenges of early detection, gaps in existing practices, and recommendations for improving diagnostic workflows.

Experimental Method

A controlled trial will involve two groups:

Experimental Group: Patients who underwent comprehensive early diagnostic testing, including advanced imaging and biomarker assessments.

Control Group: Patients who received standard diagnostic care.

Data will be collected on diagnostic accuracy, time to treatment initiation, and patient outcomes.

Study Location

Data will be collected from major healthcare institutions and cancer centers in Malaysia, including Kuala Lumpur and Shah Alam, which provide diverse and representative samples. Shah Alam offers sufficient infrastructure and access to patients through its well-established healthcare facilities. Additionally, partnerships will be established with NGOs, such as NASOM (The National Autism Society of Malaysia), and online ovarian cancer support groups for broader reach.

References: (Tajuddin et al., 2013; Karim, 2008; Fikry & Hassan, 2016)

3.6 Design of Deep Learning-Based Radiomics Feature Extraction for Ovarian Cancer

3.6.1 Learning Representation of Imaging Heterogeneity Features in Ovarian Cancer

The imaging manifestations of ovarian cancer show significant heterogeneity, characterized by the diversity of imaging features in different regions within the tumor and between tumors. This heterogeneity reflects the differences in the intrinsic histopathological and molecular biological mechanisms of the tumor, which are closely related to prognosis and treatment response. Traditional radiomics analysis mainly extracts some manually designed, globally averaged quantitative indicators, such as grayscale histograms and texture features, making it difficult to fully characterize the spatial heterogeneity of local tumor regions. In contrast, deep learning methods can learn data-driven hierarchical feature representations, adaptively modeling multi-scale semantic information from local to global and from abstract to concrete, making them powerful tools for characterizing tumor heterogeneity. This study intends to use deep convolutional neural networks for end-to-end feature learning of ovarian cancer CT and MRI images, constructing radiomic heterogeneity representations with biological interpretability. The main technical plan is as follows:

(1) Multi-scale receptive field convolutional neural network. A multi-channel convolutional neural network is designed to integrate different receptive fields, extracting image features from local to global through convolution kernels of different scales. Shallow convolution kernels have a small receptive field, extracting local texture and other detailed features; deep convolution kernels have a large receptive field, extracting features at a higher semantic level. The feature maps obtained from different receptive fields are concatenated along the channel dimension, and global average pooling can be used to obtain image features that fuse local and global representations. This method can model the multi-scale spatial heterogeneity of tumor regions from local to global in an end-to-end manner.

(2) Attention-guided region adaptive representation. To further highlight the importance differences of the internal ROI of tumors for diagnosis, an attention mechanism is introduced to learn region adaptive semantic representations. Specifically, a self-attention operation similar to Transformer is used, dividing the tumor images into equally sized patches, and learning the correlation between different regions and diagnosis through the interaction of attention weights between patches. Then, the attention weights guide the adaptive weighting of regional features, emphasizing the feature representation of significant areas and suppressing the interference from irrelevant regions. This method can model the semantic dependencies between ROIs within the tumor, enhancing the discriminability of the overall representation.

(3) Modeling the topological structure heterogeneity of graph convolutional neural networks. Convolutional neural networks are good at extracting semantic features from gridded image data, but their pixel-level regular topological structure does not align with the biological topological structure of cells and tissues within tumors. To better model the structural heterogeneity of the internal regions of tumors, graph convolutional networks (GCN) will be introduced. Tumor images are segmented into multiple irregular atomic regions based on spatial adjacency relationships and grayscale similarity, constructing the topological structure of the graph. GCN propagates imaging features on the graph structure, learning to integrate regional representations of local topological structures, better reflecting the intrinsic histopathological characteristics of tumors.

(4) Contrastive learning reveals the imaging-genotype correlation. In order to reveal the association between radiomic features and intrinsic molecular biological mechanisms, the idea of contrastive learning clustering will be adopted, mapping cases with the same subtype and similar prognosis close together in feature space, while pushing apart cases with different phenotypes. By minimizing the representation distance of similar samples and maximizing the distance between dissimilar samples, discriminative feature representations related to molecular typing and prognosis can be learned. In subsets with gene sequencing, mutations, copy number, and expression profile features can also be embedded into this contrastive learning paradigm to reveal the imaging-genotype correlation.

In summary, this study integrates cutting-edge deep learning technologies such as multi-scale convolution, attention learning, graph convolution, and contrastive clustering to characterize the intrinsic heterogeneity of ovarian cancer imaging manifestations from different dimensions, aiming to model its histopathological and molecular biological connotations. The constructed heterogeneous imaging omics features can be used for tumor subtype classification, prognosis stratification, treatment response prediction, etc., and are expected to become a new paradigm in ovarian cancer imaging omics, promoting the development of image-driven precision medicine.

3.6.2 Quantitative Analysis of Tumor Heterogeneity and Its Correlation with Prognosis

Based on the heterogeneous imaging genomics features learned in the previous section, further quantitative analysis of intra-tumor and inter-tumor heterogeneity will be conducted to construct heterogeneity metrics with prognostic predictive capabilities, guiding the precise stratification and risk assessment of ovarian cancer. The specific technical route is as follows:

(1) Quantitative analysis of intratumoral heterogeneity. Using clustering analysis methods to cluster the local radiomic features extracted from each tumor sample, different sub-regions within the tumor were identified. By setting different numbers of clusters k, intratumoral heterogeneity can be characterized at different granularities. At each clustering granularity, heterogeneity metrics can be defined, such as the proportion of the number of sub-region categories obtained from clustering to the total number of tumor regions. Through large sample statistical analysis, the correlation of this proportion with prognosis is examined, identifying heterogeneity metrics significantly related to survival time. The relationship between the imaging feature patterns of different sub-region clusters and prognosis can also be analyzed, revealing radiomic signatures associated with high recurrence and high metastasis.

(2) Quantitative analysis of inter-tumor heterogeneity. Gather global radiomic features from all tumor samples, use methods such as non-negative matrix factorization (NMF) for clustering, and identify different radiomic subtypes. Optimize the number of subtypes through cross-validation to maximize similarity within subtypes and maximize differences between subtypes. For each radiomic subtype, analyze its heterogeneity characteristics. I will help you continue this research proposal on radiomic analysis of ovarian cancer. Based on the text content and context, I will continue with "3.3.2 Quantitative analysis of inter-tumor heterogeneity."

(2) Quantitative analysis of tumor interstitial heterogeneity (continued). For each imaging radiomics subtype, analyze the association between its heterogeneity characteristic patterns and clinical pathological phenotypes. Use survival analysis methods such as Kaplan-Meier curves and Cox proportional hazards models to assess the prognostic differences among different subtypes. Combine genomic data to explore the correspondence between imaging subtypes and molecular typing, revealing the molecular biological basis of imaging heterogeneity. This helps to discover new prognostic biomarkers and provides a basis for clinical decision-making.

(3) Multi-scale heterogeneity integration analysis. Integrate modeling of heterogeneity characteristics at both intra-tumor and inter-tumor levels. A multi-task learning framework can be used to simultaneously predict local heterogeneity indicators and global subtype classifications. Alternatively, hierarchical clustering methods can be employed, first clustering intra-tumor heterogeneity and then classifying inter-tumor types based on local clustering results. Through the joint analysis of multi-scale features, a more comprehensive characterization of the heterogeneity spectrum of ovarian cancer can be achieved, leading to the discovery of biomarker combinations with clinical predictive value.

(4) Dynamic analysis of temporal heterogeneity. For cases with multiple time-point follow-up imaging, analyze the dynamic changes in tumor heterogeneity before and after treatment. Calculate the rate of change of heterogeneity features between continuous time points and assess their correlation with treatment response and prognosis. Establish a dynamic prediction model that considers the time dimension to predict the risk of tumor progression. This is of significant value for guiding adjustments to individualized treatment plans.

(5) Construction of prognostic risk prediction model. Based on the results of the aforementioned multidimensional heterogeneity analysis, a prognostic prediction model integrating multi-level features is constructed. Machine learning methods such as random forests and XGBoost are used, with tumor heterogeneity indicators, subtype classification results, and temporal dynamic features as input variables to predict patients' progression-free survival and overall survival. Through feature importance analysis, the heterogeneity feature combinations that contribute the most to prognostic prediction are selected. The model's performance is evaluated on an independent validation set to ensure its clinical application value.

Through the quantitative analysis of the heterogeneity of the above system, the imaging characteristics of ovarian cancer can be characterized from multiple dimensions, establishing a robust prognostic prediction model. These findings will help to understand the biological significance of tumor heterogeneity, providing new stratification standards and decision-making basis for precision medicine. At the same time, it also lays the foundation for subsequent clinical translational applications.

3.6.3 Knowledge-guided small sample transfer learning

In response to the issue of small sample sizes for rare pathological subtypes of ovarian cancer, this study aims to explore small sample learning strategies that integrate prior knowledge to enhance the model's generalization performance under sparse data conditions. The main technical approach includes:

(1) Transfer learning based on ImageNet pre-training. Utilizing deep convolutional neural networks pre-trained on large-scale natural image datasets to transfer their general low-level features. By using a fine-tuning strategy, only the higher-level semantically relevant network layers are fine-tuned while keeping the low-level feature extractor unchanged. This allows for the full utilization of the universal visual features learned by the pre-trained model, reducing the need for labeled data.

(2) Semi-supervised learning expands training data. For unlabelled medical imaging data, methods such as consistency regularization and pseudo-labeling are used. Data augmentation is employed to generate samples from different perspectives, forcing the model to produce consistent predictions for different transformations of the same image. This self-supervised learning strategy can fully exploit the statistical patterns in unlabelled data, enhancing the discriminative power of feature representation.

(3) Knowledge graph-guided transfer learning. Encoding radiological and pathological knowledge into structured knowledge graphs to guide model design and training processes. For example, designing topological constraints for attention mechanisms based on spatial relationships of anatomical structures, and designing hierarchical classification tasks based on pathological classification systems. The incorporation of this prior knowledge can reduce the model's dependence on training data.

(4) Joint representation learning of multimodal data. Integrating multimodal data such as clinical indicators and genomic features, using multi-view learning methods for feature fusion. By maximizing the mutual information of different modal signals, complementary feature representations are learned. This integration of multi-source information can compensate for the shortcomings of single-modal data to some extent.

(5) Active learning optimization of annotation strategies. Design an active learning scheme based on uncertainty sampling, prioritizing the selection of the most informative samples for expert annotation. Through interactive learning between the model and experts, the maximum performance improvement can be achieved with the least annotation cost. At the same time, collect expert feedback for model design improvement.

By the organic combination of the above technical routes, it is expected to achieve robust analysis of ovarian cancer imaging under small sample conditions, providing reliable auxiliary diagnostic tools for clinical practice. This is of great significance for advancing the precise diagnosis of rare pathological subtypes.

3.7 Development of Clinical Auxiliary Diagnosis System Design

3.7.1 System Architecture Design

Based on the above-mentioned imaging omics analysis methods for ovarian cancer, this study aims to develop an artificial intelligence-assisted diagnostic system that integrates image processing, feature analysis, and diagnostic decision support. The overall architecture of the system is as follows:

(1) Data Management Module: Includes PACS image data interface, DICOM parsing and processing unit, clinical electronic medical record docking interface, etc., responsible for the management functions of collecting, storing, retrieving, and displaying medical images and clinical data.

(2) Radiomics analysis engine: integrates algorithms for multi-modal image preprocessing, automatic ROI segmentation, deep learning feature extraction, and heterogeneity quantitative analysis, forming an end-to-end radiomics analysis process from data to features and from features to decisions, and uses containerization technology for packaging, providing flexible deployment and calling interfaces.

(3) Knowledge base and reasoning unit: Integrate structured knowledge such as ovarian cancer imaging genomics, clinical diagnosis and treatment guidelines, and evidence-based medical evidence to form an embodied knowledge base. Through modules such as causal reasoning and rule-based reasoning, endow the system with explainable intelligent decision-making capabilities.

(4) Human-computer interaction interface: Develop a visually friendly human-computer interaction front end based on web technology to achieve the visual display of image data and analysis results, the natural language presentation of diagnostic suggestions, and the collection of expert feedback information. The interface design follows user-centered interaction principles and takes into account the usage habits of doctors.

(5) Service interface: Follow medical information interoperability standards to develop data exchange interfaces compatible with DICOM and HL7. Provide a unified Web API that allows third-party hospital information systems to call. At the same time, develop lightweight service interfaces for mobile devices to support mobile medical applications.

3.7.2 Engineering Implementation and Optimization

The engineering implementation and optimization of the system mainly include the following aspects:

(1) Backend development: Using Python as the main development language, building RESTful API services based on web frameworks such as Flask and Django. Using databases like MongoDB and MySQL to store structured and unstructured data. Utilizing Celery for asynchronous task scheduling, with RabbitMQ as the message queue middleware to achieve parallel computing and load balancing.

(2) Front-end development: Using Vue.js as the front-end framework to achieve component-based user interface development. Utilizing visualization libraries such as D3.js and Echarts to implement interactive displays of imaging data and analysis results. Adopting UI component libraries like Material Design and Ant Design to enhance the aesthetic appeal and interactive experience of the interface.

(3) Code Quality Management: Establish a complete code version management workflow, use Git for code hosting, and GitLab for CI/CD process control. All code must be reviewed before merging. Develop a series of quality control processes such as unit testing, integration testing, and system testing. Use SonarQube for code quality scanning.

(4) Security and Privacy Protection: Strictly adhere to medical data privacy protection regulations such as HIPAA and GDPR, with full encryption of data transmission and storage. Use security protocols like OAuth 2.0 for user authentication and authorization management. Conduct third-party security audits and fix system vulnerabilities.

(5) System integration and deployment: Use containerization technologies such as Docker and Kubernetes to achieve standardized packaging and orchestration of system components. Build a Jenkins pipeline to achieve automated integration, testing, delivery, and deployment. Choose IaaS and PaaS platforms (such as Alibaba Cloud, Amazon AWS, etc.) to achieve elastic scaling and load balancing.

3.7.3 System Applications and Optimization

This system will be piloted in multiple hospitals and continuously optimized in practice. The main applications and optimization strategies include:

(1) Process integration: Seamlessly integrate the system into the doctor's treatment workflow, minimizing interference with their work habits. Streamline the data flow of various stages such as consultation, examination, diagnosis, treatment, and follow-up, integrating it into the system's data management module. Optimize system response time, increase concurrent throughput, and ensure that treatment efficiency is not affected.

(2) Expert feedback learning: Collect feedback from doctors on system diagnostic suggestions and record their corrective opinions on each case diagnosis. Use the feedback data to train the model and learn from the experts' diagnostic experience. Establish an iterative optimization mechanism for human-machine collaborative diagnosis.

(3) Model update: Regularly retrain the model using newly collected data to continuously improve performance. Establish an automated data processing and feature extraction workflow to shorten the model update cycle. Develop a model hot update mechanism to smoothly upgrade without interrupting online services.

(4) Radiomics quality control: Conduct quality assessment of the system's radiomics analysis results in conjunction with clinical practice. For cases with significant errors, manual review and annotation are performed, and these cases are included in subsequent model training. By establishing standardized operating procedures for radiomics analysis, guide data collection and processing to ensure the accuracy and reliability of the analysis results.

(5) Application results promotion: Based on the good effects achieved in pilot applications, promote the system to more medical institutions and establish a regional medical collaboration network. Hold training sessions, write user manuals, and cultivate doctors' ability to use the system. At the same time, actively participate in academic conferences, publish application result papers, and expand the academic influence of the system.

Through the closed-loop iteration of system development, application, and optimization mentioned above, it is expected to form a practical ovarian cancer intelligent diagnosis program, which will make a substantial contribution to improving the diagnosis and treatment level of ovarian cancer and promoting the development of smart healthcare. At the same time, the accumulated experience can also be applied to the intelligent diagnosis of other tumors, enhancing the breadth and depth of artificial intelligence benefiting public health.

3.8cloclusion

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