Competing Dual-Network with Pseudo-Supervision Rectification for Semi-Supervised Medical Image Segmentation

Semi-supervised medical image segmentation utilizes a large number of unlabeled images in combination with a limited number of labeled images for model training and optimization, significantly reducing the reliance on large-scale labeled images. However, due to the model's cognitive biases, distribution gap between labeled and unlabeled images, and potential noise in the pseudo-supervision process, learning robust representations from a large number of unlabeled images is still a challenging task. To address these issues, we propose a new framework of Competing Dual-Network with Pseudo-Supervision Rectification (CDPR), which integrates the bidirectional copy-paste mechanism for single image pair and the pseudo-supervision rectification strategy into the architecture of the competing dual-network. Through the competing dual-network, we encourage two segmentation networks to engage in mutual learning and competition, which contributes to break the model's cognitive biases. We utilize the bidirectional copy-paste technique for single image pair to establish a consistent learning strategy for both labeled and unlabeled data, thereby better aligning the data distribution. Finally, by optimizing the pseudo-supervised loss, the negative impact of potential noise on the model's segmentation performance during the pseudo-supervision stage is effectively alleviated. Experimental results on the benchmark dataset demonstrate that our method achieves outstanding performance compared to several state-of-the-art methods.