HCPSNet: heterogeneous cross-pseudo-supervision network with confidence evaluation for semi-supervised medical image segmentation

Medical image segmentation technology can effectively help doctors to diagnose, but there are too little annotated data, which limits the development of fully supervised medical image segmentation methods. This dilemma leads to urgent research on semi-supervised medical image segmentation methods. To cope with this dilemma, we propose a semi-supervised dual flow network, which is called the Heterogeneous Cross-pseudo-supervision Network (HCPSNet). In the HCPSNet, Unet and Swin-Unet are combined for cross-learning, and a shifted patch tokenization (SPT) module is embedded into Swin-Unet to increase the spatial information contained in the feature maps. Besides, a confidence evaluation (CE) module is present to improve the performance of the model. The experimental results on three medical clinical datasets, LA2018, BraTs2019, and ACDC, show that our method can achieve good segmentation results with limited labeled samples. The mean dice of our proposed network on ACDC with seven cases’ samples is 86.17%, about 3% higher than other models.