Uncertainty-Guided Different Levels of Pseudolabels for Semisupervised Medical Image Segmentation

The significance of low-quality data in unlabeled medical images is always underestimated. We believe that these underestimated data contain valuable information that remains largely unexplored. We present a novel uncertainty-guided different levels of pseudolabels (UDLP) framework to explore the underestimated data in medical images. The framework consists of a student-teacher model that uses uncertainty to classify the pseudolabels predicted by the teacher model into three levels: high confidence, low confidence, and unreliability. The student model learns directly from high-confidence pseudolabels. By using the confident learning method in low-confidence pseudolabels, the teacher model corrects the noisy labels in low-confidence voxels to provide positive feature information for the student model. We design a method for removing unreliable pseudolabels, to further enhance model's generalizability. The proposed framework UDLP is evaluated on two datasets and demonstrates superior performance compared to other state-of-the-art methods.