  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.
