Skin lesion segmentation from dermoscopy images is of great significance in the quantitative analysis of skin cancers, which is yet challenging even for dermatologists due to the inherent issues, i.e., considerable size, shape and color variation, and ambiguous boundaries. Recent vision transformers have shown promising performance in handling the variation through global context modeling. Still, they have not thoroughly solved the problem of ambiguous boundaries as they ignore the complementary usage of the boundary knowledge and global contexts. In this paper, we propose a novel cross-scale boundary-aware transformer, \textbf{XBound-Former}, to simultaneously address the variation and boundary problems of skin lesion segmentation. XBound-Former is a purely attention-based network and catches boundary knowledge via three specially designed learners. We evaluate the model on two skin lesion datasets, ISIC-2016\&PH$^2$ and ISIC-2018, where our model consistently outperforms other convolution- and transformer-based models, especially on the boundary-wise metrics. We extensively verify the generalization ability of polyp lesion segmentation that has similar characteristics, and our model can also yield significant improvement compared to the latest models.

本文提出了一种创新的跨尺度边界感知变压器XBound-Former，通过三个特别设计的学习器获取边界知识，并在两个皮肤病例数据集(ISIC-2016和PH2以及ISIC-2018)上进行了评估。结果显示，该模型在皮肤病变量方面的表现优于其他卷积和变压器模型，在类似的息肉病变分割方面也具有较强的实用价值。

XBound-Former：基于Transformer的跨尺度边界建模方法