XAI for Skin Cancer Detection with Prototypes and Non-Expert Supervision
Miguel Correia Alceu Bissoto Carlos Santiago Catarina Barata
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.