Improving Semi-Supervised Medical Image Segmentation through Students Discrepancy-Informed Correction Learning

The proposed Students Discrepancy-Informed Correction Learning (SDCL) framework leverages the segmentation discrepancies between two structurally different student models to guide the correction of confirmation and cognitive biases in semi-supervised medical image segmentation.