Explainable Artificial Intelligence Architecture for Melanoma Diagnosis Using Indicator Localization and Self-Supervised Learning

TL;DR

This paper presents an explainable deep learning architecture for melanoma diagnosis that provides interpretable visual explanations, improving trust and clinical relevance over existing models.

Contribution

The authors introduce a novel explainable AI architecture utilizing indicator localization and self-supervised learning for melanoma detection, enhancing interpretability.

Findings

Matches clinical explanations significantly better than existing architectures

Provides clinically interpretable visual explanations

Achieves high accuracy in melanoma classification

Abstract

Melanoma is a prevalent lethal type of cancer that is treatable if diagnosed at early stages of development. Skin lesions are a typical indicator for diagnosing melanoma but they often led to delayed diagnosis due to high similarities of cancerous and benign lesions at early stages of melanoma. Deep learning (DL) can be used as a solution to classify skin lesion pictures with a high accuracy, but clinical adoption of deep learning faces a significant challenge. The reason is that the decision processes of deep learning models are often uninterpretable which makes them black boxes that are challenging to trust. We develop an explainable deep learning architecture for melanoma diagnosis which generates clinically interpretable visual explanations for its decisions. Our experiments demonstrate that our proposed architectures matches clinical explanations significantly better than existing architectures.