Explainable Artificial Intelligence Architecture for Melanoma Diagnosis Using Indicator Localization and Self-Supervised Learning
This addresses the challenge of trust in AI for clinicians by improving interpretability in melanoma diagnosis, though it is incremental as it builds on existing explainable AI techniques.
The paper tackles the problem of uninterpretable deep learning models in melanoma diagnosis by developing an explainable architecture that generates clinically interpretable visual explanations, demonstrating it matches clinical explanations significantly better than existing methods.
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.