Update README.md

README.md

@@ -4,7 +4,7 @@ Official PyTorch implementation of the MICCAI 2023 paper and a domain generaliza
 [[`arXiv`](https://arxiv.org/pdf/2304.01508.pdf)]
 [[`BibTex`](#citation)]
 [[`MICCAI paper`](https://link.springer.com/chapter/10.1007/978-3-031-43990-2_24)]
-[[`Extened journal paper`](https://arxiv.org/pdf/2401.03002.pdf)]
+[[`Extended journal paper`](https://arxiv.org/pdf/2401.03002.pdf)]
 
 ## Introduction
 **[abstract]** *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.*