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. Our code and dataset will be released
at https://github.com/SiyuanYan1/EPVT.Comment: 12 pages, 5 figure