This paper presents a comprehensive review of recent advances in automated ovarian cancer diagnosis using histopathological images through deep learning approaches. It examines methodologies such as dual attention mechanisms, multi-modal integration, and explainable AI techniques, analyzed across 13 recent studies. The review highlights the transition from traditional convolutional neural network (CNN) architectures to advanced approaches that incorporate clinical data and multi-omics integration, achieving diagnostic accuracy rates up to 99.01%. Our findings suggest that combining multiple data modalities and implementing attention-based mechanisms significantly enhances diagnostic accuracy and interpretability.
Miya et al. (Fri,) studied this question.