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.
翻译:在皮肤癌的定量分析中,皮肤癌的皮肤部位偏差部分的皮肤部位分析非常重要,因为皮肤癌的内在问题,即尺寸、形状和颜色差异以及模糊的边界,对皮肤部位也具有挑战性。最近的视觉变压器在通过全球背景建模处理差异方面表现良好。但是,它们没有彻底解决模糊的边界问题,因为它们忽视了边界知识和全球背景的互补使用。在本文件中,我们提议建立一个新的跨尺度边界变压器,\ textbf{XBound-Former},同时解决皮肤部位的变异和边界问题。XBound-Former是一个纯粹基于关注的网络,通过三个专门设计的学习者掌握边界知识。我们评估了两个皮肤变异数据集的模型,即ISIC-2016 ⁇ 2美元和ISIC-2018美元,我们的模型始终优于其他以变异和变异为基础的模型,特别是边界测量仪。我们广泛核查了聚变异变异模型的一般化能力,这些模型也具有类似的特性,可以比较我们最新的模型。