Evaluating the Interpretability of Prototype Networks for Medical Image Analysis

Artificial intelligence (AI) holds great potential for assisting physicians in medical image analysis, yet current models provide predictions without transparent explanations of their decision-making process. Explainable AI (XAI) aims to address this issue by providing tools to enhance model interpretability. However, existing XAI methods, such as post-hoc explanation techniques, often produce coarse heatmaps that lack the precision needed for medical applications. In contrast, self-explaining models like prototype networks offer a promising approach by learning characteristic image patterns, called prototypes. These prototypes could aid physicians in their diagnostic process. In this work, we evaluate the interpretability of prototype networks in the context of medical image classification using a mammography and a colorectal polyp (CP) dataset. We assess the quality and relevance of the learned prototypes by comparing them to domain-specific knowledge and physician-annotated bounding boxes. Our key contribution involves linking these prototypes to textual descriptions of CPs. Our experiments demonstrate that learned prototypes visually correspond to clinically relevant regions of interest (ROIs), obtaining a mean intersection over union (IoU) of 0.25 ± 0.29. Challenges remain in improving spatial accuracy and linking prototypes to textual descriptions. We discuss these limitations and propose future directions for integrating prototype networks into clinical workflows, contributing to the development of transparent and trustworthy XAI systems for clinical decision support.