Skin cancer detection through dermoscopy image analysis is a critical task. However, existing models used for this purpose often lack interpretability and reliability, raising the concern of physicians due to their black-box nature. In this paper, we propose a novel approach for the diagnosis of melanoma using an interpretable prototypical-part model. We introduce a guided supervision based on non-expert feedback through the incorporation of: 1) binary masks, obtained automatically using a segmentation network; and 2) user-refined prototypes. These two distinct information pathways aim to ensure that the learned prototypes correspond to relevant areas within the skin lesion, excluding confounding factors beyond its boundaries. Experimental results demonstrate that, even without expert supervision, our approach achieves superior performance and generalization compared to non-interpretable models.

翻译：通过皮肤镜图像分析进行皮肤癌检测是一项关键任务。然而，当前用于此目的的模型往往缺乏可解释性和可靠性，其黑箱特性引发了医生的担忧。本文提出了一种基于可解释原型-局部模型进行黑色素瘤诊断的新方法。我们引入了一种基于非专家反馈的引导式监督机制，具体包括：1）利用分割网络自动生成的二值掩膜；2）经用户精炼的原型。这两条不同的信息通路旨在确保学习到的原型对应于皮肤病变内的相关区域，同时排除病变边界之外的混淆因素。实验结果表明，即使在没有专家监督的情况下，相较于不可解释模型，我们的方法仍能实现更优越的性能和泛化能力。