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：皮肤病识别在深度学习中取得了显著的进展，有越来越大的需求将这些系统部署在现实场景中。然而，最近的研究表明，皮肤病识别的深度神经网络可能过于依赖与疾病无关的图像晕染（例如：黑暗的角落、浓密的毛发等），导致在未知环境中表现不佳。为了解决这个问题，我们提出了一种称为“环境感知提示视觉转换器（EPVT）”的新颖领域推广方法，其中将提示嵌入视觉转换器以从各种领域协同学习知识。具体而言，EPVT利用一组领域提示，每个提示都扮演一个领域专家的角色，以捕获领域特定的知识；以及一个共享提示用于全数据集的通用知识。为了促进知识共享和不同提示之间的交互，我们引入了一个领域提示生成器，它可以实现领域提示和共享提示之间的低秩乘法更新。此外，还制定了一种领域混合策略，以减少每个领域中共存的图像晕染，从而允许更灵活的决策界限并减轻错误分配领域标签的问题。在四个分布外数据集和六个不同偏差的ISIC数据集上的实验表明，EPVT在各种环境中皮肤病识别具有卓越的推广能力。我们的代码和数据集将在https://github.com/SiyuanYan1/EPVT公开发布。