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.

通过可解释的原型 - 部分模型以及自动分割网络和用户优化的原型引入的非专家反馈指导，我们提出了一种新的黑素瘤诊断方法，实验证明即使没有专家监督，我们的方法在性能和泛化程度上均优于不可解释的模型。