  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.
