ExAID: A Multimodal Explanation Framework for Computer-Aided Diagnosis of Skin Lesions

TL;DR

ExAID is a novel multimodal explanation framework for skin lesion diagnosis that combines textual and visual explanations to improve transparency and trust in AI-assisted dermatology.

Contribution

This work introduces ExAID, a framework that provides clear, multi-modal explanations for AI decisions in skin lesion diagnosis using concept-based methods.

Findings

ExAID offers effective multi-modal explanations even for incorrect predictions.

Quantitative and qualitative evaluations demonstrate the utility of ExAID in clinical scenarios.

ExAID enhances understanding and trust for dermatologists using AI tools.

Abstract

One principal impediment in the successful deployment of AI-based Computer-Aided Diagnosis (CAD) systems in clinical workflows is their lack of transparent decision making. Although commonly used eXplainable AI methods provide some insight into opaque algorithms, such explanations are usually convoluted and not readily comprehensible except by highly trained experts. The explanation of decisions regarding the malignancy of skin lesions from dermoscopic images demands particular clarity, as the underlying medical problem definition is itself ambiguous. This work presents ExAID (Explainable AI for Dermatology), a novel framework for biomedical image analysis, providing multi-modal concept-based explanations consisting of easy-to-understand textual explanations supplemented by visual maps justifying the predictions. ExAID relies on Concept Activation Vectors to map human concepts to those learnt by arbitrary Deep Learning models in latent space, and Concept Localization Maps to highlight concepts in the input space. This identification of relevant concepts is then used to construct fine-grained textual explanations supplemented by concept-wise location information to provide comprehensive and coherent multi-modal explanations. All information is comprehensively presented in a diagnostic interface for use in clinical routines. An educational mode provides dataset-level explanation statistics and tools for data and model exploration to aid medical research and education. Through rigorous quantitative and qualitative evaluation of ExAID, we show the utility of multi-modal explanations for CAD-assisted scenarios even in case of wrong predictions. We believe that ExAID will provide dermatologists an effective screening tool that they both understand and trust. Moreover, it will be the basis for similar applications in other biomedical imaging fields.