Decoding diagnosis : AI explainability for enhanced skin cancer detection

Abstract

Skin cancer is one of the most common cancers worldwide and is primarily diagnosed through visual examination. With the availability of large amounts of dermoscopic data, recent advancements in artificial intelligence (AI) have achieved remarkable accuracy in skin cancer classification. However, due to the black-box nature of deep learning models, dermatologists often struggle to understand the underlying decision-making process, limiting the transparency and interpretability of AI-driven diagnoses. In this work, we investigate advancements in Prototypical Part Networks (ProtoPNet) to skin cancer detection by applying the Pixel-Grounded Prototypical Part Network (PIXPNET), designed to address the challenge of pixel-space mapping in prototype projection. The PIXPNET architecture was trained and evaluated to assess its generalizability. Our results show that PIXPNET significantly outperforms ProtoPNet for skin cancer detection in a multi-class classification setting. Additionally, we analyze the learned prototypes to assess their relevance to input images, demonstrating improved interpretability compared to its counterpart, ProtoPNet.