  Skin lesion recognition using deep learning has made remarkable progress, and
there is an increasing need for deploying these systems in real-world
scenarios. However, recent research has revealed that deep neural networks for
skin lesion recognition may overly depend on disease-irrelevant image artifacts
(i.e., dark corners, dense hairs), leading to poor generalization in unseen
environments. To address this issue, we propose a novel domain generalization
method called EPVT, which involves embedding prompts into the vision
transformer to collaboratively learn knowledge from diverse domains.
Concretely, EPVT leverages a set of domain prompts, each of which plays as a
domain expert, to capture domain-specific knowledge; and a shared prompt for
general knowledge over the entire dataset. To facilitate knowledge sharing and
the interaction of different prompts, we introduce a domain prompt generator
that enables low-rank multiplicative updates between domain prompts and the
shared prompt. A domain mixup strategy is additionally devised to reduce the
co-occurring artifacts in each domain, which allows for more flexible decision
margins and mitigates the issue of incorrectly assigned domain labels.
Experiments on four out-of-distribution datasets and six different biased ISIC
datasets demonstrate the superior generalization ability of EPVT in skin lesion
recognition across various environments. Code is avaliable at
https://github.com/SiyuanYan1/EPVT.
