Breast Cancer Diagnosis: A Comprehensive Exploration of Explainable Artificial Intelligence (XAI) Techniques
Samita Bai, Sidra Nasir, Rizwan Ahmed Khan, Alexandre Meyer, Hubert Konik
TL;DR
This review surveys Explainable AI approaches in breast cancer diagnosis across imaging, histopathology, and genomics, organizing methods into SHAP, CAM/Grad-CAM, LIME, and additional techniques. It assesses datasets and modalities used in BC research, discusses model-agnostic versus model-specific explanations, and identifies the need for standardized evaluation metrics and clinical workflow integration. The findings illustrate how XAI can improve transparency, trust, and personalized decision-making, while also highlighting challenges such as data diversity, computational demands, and potential misalignment with clinical relevance. Overall, the paper underscores XAI's potential to bridge complex AI models and practical breast cancer care, while outlining concrete avenues for rigorous evaluation and deployment in healthcare settings.
Abstract
Breast cancer (BC) stands as one of the most common malignancies affecting women worldwide, necessitating advancements in diagnostic methodologies for better clinical outcomes. This article provides a comprehensive exploration of the application of Explainable Artificial Intelligence (XAI) techniques in the detection and diagnosis of breast cancer. As Artificial Intelligence (AI) technologies continue to permeate the healthcare sector, particularly in oncology, the need for transparent and interpretable models becomes imperative to enhance clinical decision-making and patient care. This review discusses the integration of various XAI approaches, such as SHAP, LIME, Grad-CAM, and others, with machine learning and deep learning models utilized in breast cancer detection and classification. By investigating the modalities of breast cancer datasets, including mammograms, ultrasounds and their processing with AI, the paper highlights how XAI can lead to more accurate diagnoses and personalized treatment plans. It also examines the challenges in implementing these techniques and the importance of developing standardized metrics for evaluating XAI's effectiveness in clinical settings. Through detailed analysis and discussion, this article aims to highlight the potential of XAI in bridging the gap between complex AI models and practical healthcare applications, thereby fostering trust and understanding among medical professionals and improving patient outcomes.