Breast tumor segmentation network based on local attention

Motivation: Accurate segmentation of the lesion region is the first step toward early diagnosis. The transformer, on the other hand, has very competitive performance but also extremely high computational complexity. Goal(s): Finding an efficient and computationally inexpensive method is currently a great challenge for the application of transformers in medical image segmentation. Approach: We adopt the shift local self-attention method to extract features, which reduces the computational complexity while obtaining very high segmentation accuracy. Results: Experimental results on a dataset comprising 130 breast tumor cases demonstrate that the proposed network accurately segments breast tumors, surpassing the accuracy of many other convolution-based or transformer-based networks. Impact: This study may inspire scientists to create simpler, efficient components for reduced self-attention computational cost while preserving long-range modeling. The achievement in high-precision segmentation can ease clinicians' workload by reducing image annotation.