Nodule Segmentation Method Based on Swin Transformer and UNet
Accurate segmentation of pulmonary nodules is the key to subsequent benign and malignant analysis and diagnosis.Because the segmentation model based on convolutional neural network is limited by local feature extrac-tion,the global feature is ignored.Therefore,this paper proposes a new semantic segmentation framework for pulmo-nary nodules ST-UNet network,and emparts Swin transformer into UNet to form a novel dual encoder structure of Swin Transformer and CNN in parallel.The results show that this model not only has a good performance in the seg-mentation of pulmonary nodules,but also has important clinical significance and application value for doctors in the early diagnosis of pulmonary nodules.
万方数据
维普
