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.

翻译：基于深度学习的皮肤病变识别已取得显著进展，将这些系统部署到真实场景的需求日益增长。然而，近期研究表明，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），从而导致在未见环境中的泛化能力较差。为解决此问题，我们提出一种名为EPVT的新型领域泛化方法，该方法通过将提示嵌入视觉Transformer中，以协同学习来自不同领域知识。具体而言，EPVT利用一组领域提示（每个提示扮演领域专家角色）捕获领域特定知识，并利用一个共享提示捕获整个数据集的通用知识。为促进知识共享与不同提示间的交互，我们引入领域提示生成器，实现领域提示与共享提示之间的低秩乘法更新。此外，我们设计了领域混合策略以减少每个领域中伴随出现的伪影，从而获得更灵活的决策边界，并缓解领域标签错误分配的问题。在四个分布外数据集及六个不同偏置ISIC数据集上的实验证明，EPVT在多种环境下针对皮肤病变识别具有优越的泛化能力。我们的代码与数据集将在https://github.com/SiyuanYan1/EPVT开源。