Co‐training semi‐supervised medical image segmentation based on pseudo‐label weight balancing

We proposed a weight-balanced co-trained cross-consistent semi-supervised model SCMT for semi-supervised segmentation of medical images, which consists of a CMT (Co-training Mean Teacher) structure and quantity-quality-balanced pseudo-label-guided mutual consistency constraints. Compared with other models, we effectively exploit the challenging region and can more accurately capture the contours and finer details of the segmented objects without any shape or boundary constraints, resulting in highly accurate and detail-rich segmentation results. In addition, we conduct comparative experiments with existing semi-supervised models, and the experimental results show that our proposed model is capable of handling complex structures and segmenting details commonly missed by other methods. The segmentation results obtained are relatively stable and consistent and have certain advantages in improving the performance of surface segmentation. Code is available at: https://github.com/zhaojiashi/SCMT.