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.

翻译：黑色素瘤的早期检测对于预防严重并发症和提高治疗成功率至关重要。现有的用于黑色素瘤皮肤病变诊断的深度学习方法被视为黑盒模型，因为它们忽略了模型预测背后的推理过程，损害了这些诊断方法的可信度和可接受性。现有的基于概念的解释方法依赖于事后方法，即借助额外模型推导解释结果。本文提出了一种本质可解释的框架，通过引入硬注意力机制和一致性损失项，确保概念编码器生成的概念激活在视觉上具有一致性，从而提升基于概念模型的可解释性，且无需额外标注的监督。该框架以人类可解释的概念及其对最终预测的各自贡献进行决策解释，同时提供概念在图像中存在位置的视觉解释。在皮肤图像数据集上的实验表明，我们的方法在皮肤病变分类任务上优于现有的黑盒模型和基于概念的模型。