# Safer Skin Lesion Classification with Global Class Activation Probability Map Evaluation and SafeML

## TL;DR

This paper introduces a new explainability method for skin lesion classification that analyzes class activation maps probabilistically and employs SafeML to detect false diagnoses, aiming to improve trustworthiness and safety in AI diagnostics.

## Contribution

It proposes Global Class Activation Probabilistic Map Evaluation and SafeML, enhancing reliability and interpretability of skin lesion classification models beyond existing methods.

## Key findings

- Improved detection of false diagnoses.
- Enhanced interpretability of model decisions.
- Validated on ISIC datasets with MobileNetV2 and Vision Transformers.

## Abstract

Recent advancements in skin lesion classification models have significantly improved accuracy, with some models even surpassing dermatologists' diagnostic performance. However, in medical practice, distrust in AI models remains a challenge. Beyond high accuracy, trustworthy, explainable diagnoses are essential. Existing explainability methods have reliability issues, with LIME-based methods suffering from inconsistency, while CAM-based methods failing to consider all classes. To address these limitations, we propose Global Class Activation Probabilistic Map Evaluation, a method that analyses all classes' activation probability maps probabilistically and at a pixel level. By visualizing the diagnostic process in a unified manner, it helps reduce the risk of misdiagnosis. Furthermore, the application of SafeML enhances the detection of false diagnoses and issues warnings to doctors and patients as needed, improving diagnostic reliability and ultimately patient safety. We evaluated our method using the ISIC datasets with MobileNetV2 and Vision Transformers.