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.

翻译：基于深度学习的皮肤病变识别已取得显著进展，且在实际场景中部署此类系统的需求日益增长。然而，最新研究表明，用于皮肤病变识别的深度神经网络可能过度依赖与疾病无关的图像伪影（如暗角、密集毛发），导致在未见环境中的泛化性能较差。为解决该问题，我们提出了一种新颖的领域泛化方法EPVT，其通过将提示嵌入视觉Transformer中，协同学习来自不同领域的知识。具体而言，EPVT利用一组领域提示（每个提示充当领域专家）捕获领域特有知识，并利用共享提示学习整个数据集上的通用知识。为促进不同提示间的知识共享与交互，我们引入了领域提示生成器，该生成器支持领域提示与共享提示之间的低秩乘法更新。此外，我们还设计了一种领域混合策略以减少每个领域中的共现伪影，从而允许更灵活的决策边界，并缓解领域标签错误分配的问题。在四个分布外数据集和六个不同偏置的ISIC数据集上的实验表明，EPVT在多种环境下对皮肤病变识别具有卓越的泛化能力。代码发布于https://github.com/SiyuanYan1/EPVT。