Semi-supervised Medical Image Segmentation with Multiscale Contrastive Learning and Cross-Supervision

We propose a semi-supervised segmentation method based on multiscale contrastive learning to solve the problem of shortage of annotations in medical image segmentation tasks. We apply perturbations to the input image and encoded features and make the output as consistent as possible by cross-supervision, which is a way to improve the generalizability of the model. Two scales of contrastive learning, patch-level and pixel-level, are employed to enhance the intra-class compactness and inter-class separability of the features. We evaluate the proposed model using three public datasets for brain tumor,left atrial, and cellular nuclei segmentation. The experiments showed that our model outperforms state-of-the-art methods.