Coherent Concept-based Explanations in Medical Image and Its Application to Skin Lesion Diagnosis
This addresses the need for trustworthy and acceptable diagnostic methods in medical imaging, specifically for skin lesion classification, though it is incremental as it builds on concept-based approaches.
The paper tackles the problem of black-box deep learning models in melanoma skin lesion diagnosis by proposing an inherently interpretable framework that uses a hard attention mechanism and coherence loss to provide concept-based explanations without extra annotations, and it outperforms existing models on skin image datasets.
Early detection of melanoma is crucial for preventing severe complications and increasing the chances of successful treatment. Existing deep learning approaches for melanoma skin lesion diagnosis are deemed black-box models, as they omit the rationale behind the model prediction, compromising the trustworthiness and acceptability of these diagnostic methods. Attempts to provide concept-based explanations are based on post-hoc approaches, which depend on an additional model to derive interpretations. In this paper, we propose an inherently interpretable framework to improve the interpretability of concept-based models by incorporating a hard attention mechanism and a coherence loss term to assure the visual coherence of concept activations by the concept encoder, without requiring the supervision of additional annotations. The proposed framework explains its decision in terms of human-interpretable concepts and their respective contribution to the final prediction, as well as a visual interpretation of the locations where the concept is present in the image. Experiments on skin image datasets demonstrate that our method outperforms existing black-box and concept-based models for skin lesion classification.