Semi-supervised medical image segmentation has recently achieved great success, but assigning trustworthy pseudo-labels to unlabeled images has been a difficult problem in medical image processing. A common solution is to select reliable predicted pixels as the pseudo-labels. However, unreliable pixels are often concentrated in the edge areas of the foreground and background in medical tasks. Directly discarding these pixels will result in this important information never being available. The foreground of medical images is usually surrounded by the edge area. This section of pixels is a mixture of the two categories, which makes it very difficult to distinguish. To address these problems, we propose a semi-supervised medical segmentation framework that combines conformal prediction and contrastive learning. Our framework can use conformal prediction to select pseudo-labels with high confidence and preserve important boundary information. Furthermore, the segmentation performance of edge regions can be improved using contrastive learning between edge categories and non-edge categories. Extensive experiments on multiple benchmarks show that our framework consistently outperforms state-of-the-art methods.
Shi et al. (Fri,) studied this question.