Weakly supervised learning and interpretability for endometrial whole slide image diagnosis

TL;DR: In this paper , a fully supervised convolutional neural network (CNN) model was trained to calculate the probability of a patch from the slide being "malignant" or "other or benign".

TL;DR: In this article , the authors highlight advances in artificial intelligence (AI) technologies in five areas: Collaborative AI, Multimodal AI, Human-Centered AI, Equitable AI and Ethical and Value-based AI in order to cope with future complex socioeconomic and public health issues.

Abstract: OBJECTIVE Explainable Artificial Intelligence (XAI) is increasingly recognized as a crucial tool in cancer care, with significant potential to enhance diagnosis, prognosis, and treatment planning. However, the holistic integration of XAI across all stages of cancer care remains underexplored. This review addresses this gap by systematically evaluating the role of XAI in these critical areas, identifying key challenges and emerging trends. MATERIALS AND METHODS Following the PRISMA guidelines, a comprehensive literature search was conducted across Scopus and Web of Science, focusing on publications from January 2020 to May 2024. After rigorous screening and quality assessment, 69 studies were selected for in-depth analysis. RESULTS The review identified critical gaps in the application of XAI within cancer care, notably the exclusion of clinicians in 83% of studies, which raises concerns about real-world applicability and may lead to explanations that are technically sound but clinically irrelevant. Additionally, 87% of studies lacked rigorous evaluation of XAI explanations, compromising their reliability in clinical practice. The dominance of post-hoc visual methods like SHAP, LIME and Grad-CAM reflects a trend toward explanations that may be inherently flawed due to specific input perturbations and simplifying assumptions. The lack of formal evaluation metrics and standardization constrains broader XAI adoption in clinical settings, creating a disconnect between AI development and clinical integration. Moreover, translating XAI insights into actionable clinical decisions remains challenging due to the absence of clear guidelines for integrating these tools into clinical workflows. CONCLUSION This review highlights the need for greater clinician involvement, standardized XAI evaluation metrics, clinician-centric interfaces, context-aware XAI systems, and frameworks for integrating XAI into clinical workflows for informed clinical decision-making and improved outcomes in cancer care.

TL;DR: A patch-based whole slide image analysis method was implemented and it was discovered that the most successful method uses a patch size of 256 × 256 pixels with the informed sampling method, using the location of tumour regions to sample a balanced dataset.

TL;DR: In this article, a clustering-constrained-attention multiple-instance learning (CLAM) method is proposed to identify subregions of high diagnostic value to accurately classify whole slides and instance level clustering over the identified representative regions to constrain and refine the feature space.

TL;DR: A comprehensive survey of the characteristics which define and differentiate the types of MIL problems is provided, providing insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research.

TL;DR: In this article, a taxonomy for interpretability of DNNs is proposed, as well as applications of interpretability in medicine and future research directions, such as in relation to fuzzy logic and brain science.

TL;DR: The design and implementation of the virtual microscope, a software system employing a client/server architecture to provide a realistic emulation of a high power light microscope, provides a form of completely digital telepathology, allowing simultaneous access to archived digital slide images by multiple clients.