EAswin-unet: Segmenting CT images of COVID-19 with edge-fusion attention

Guilin Zhan, Kai Qian, Wenyang Chen, Dandan Xue, Mengdi Li, Jun Zhang, Yonghang Tai

Abstract: Highlights•We propose the EAswin-Unet model based on edge attention, which enables us to obtain better position information and edge details of lesions.•By combining attention and pixel-weighted loss function, we improve the model’s focus on local information during the training process and increase the accuracy of COVID-19 lesion segmentation.•We propose a replacement-based semi-supervised strategy for training the model. This strategy helps alleviate the shortcomings of the original model in small data labels, such as inadequate data, low model fitting ability, and poor generalization ability during training.