Skin lesion segmentation from dermoscopy images is of great importance for improving the quantitative analysis of skin cancer. However, the automatic segmentation of melanoma is a very challenging task owing to the large variation of melanoma and ambiguous boundaries of lesion areas. While convolutional neutral networks (CNNs) have achieved remarkable progress in this task, most of existing solutions are still incapable of effectively capturing global dependencies to counteract the inductive bias caused by limited receptive fields. Recently, transformers have been proposed as a promising tool for global context modeling by employing a powerful global attention mechanism, but one of their main shortcomings when applied to segmentation tasks is that they cannot effectively extract sufficient local details to tackle ambiguous boundaries. We propose a novel boundary-aware transformer (BAT) to comprehensively address the challenges of automatic skin lesion segmentation. Specifically, we integrate a new boundary-wise attention gate (BAG) into transformers to enable the whole network to not only effectively model global long-range dependencies via transformers but also, simultaneously, capture more local details by making full use of boundary-wise prior knowledge. Particularly, the auxiliary supervision of BAG is capable of assisting transformers to learn position embedding as it provides much spatial information. We conducted extensive experiments to evaluate the proposed BAT and experiments corroborate its effectiveness, consistently outperforming state-of-the-art methods in two famous datasets.

翻译：在皮肤癌的定量分析中,皮肤镜像的皮肤损伤偏差部分对于改善皮肤癌的定量分析非常重要。然而,由于黑皮瘤和偏差的界限差异很大,对黑皮瘤的自动分割是一项非常艰巨的任务。虽然革命中立网络(CNNs)在这项任务中取得了显著进展,但大多数现有解决方案仍然无法有效捕捉全球依赖性,以抵消有限的接受领域造成的诱导偏差。最近,通过使用强大的全球关注机制,变压器被提议为全球背景建模的一个很有希望的工具,但是,在应用分层任务时,变压器的主要缺点之一是,它们无法有效地提取足够的当地细节来解决模糊的边界。我们提议建立一个新的边界观测变压器(BAT),以全面应对自动皮肤损伤分割的挑战。具体地说,我们把新的边界偏移引力门(BAG)纳入变压器,以使整个网络不仅能够有效地模拟全球长距离依赖性依赖性成型,而且同时通过充分利用边界前知识来捕捉到更多的本地细节。特别是,我们提议了一个新的边界观察变压式变压实验系统,能够对BAAT进行长期的实验。我们不断的实验。