While deep networks have achieved broad success in analyzing natural images, when applied to medical scans, they often fail in unexpected situations.

Machine learning (ML) is pervasively deployed.

Specifically, a set of prototypes is optimized to achieve per-task prototype overfit-ting, enabling accurately obtaining the overfitted prototypes for individual tasks. Furthermore, we introduce a task-guided diffusion process within the prototype space, enabling the meta-learning of a generative process that transitions from a vanilla prototype to an overfitted prototype.

Dermnet-S chooses the top-30 classes for meta-training.