  Semi-supervised learning has demonstrated great potential in medical image
segmentation by utilizing knowledge from unlabeled data. However, most existing
approaches do not explicitly capture high-level semantic relations between
distant regions, which limits their performance. In this paper, we focus on
representation learning for semi-supervised learning, by developing a novel
Multi-Scale Cross Supervised Contrastive Learning (MCSC) framework, to segment
structures in medical images. We jointly train CNN and Transformer models,
regularising their features to be semantically consistent across different
scales. Our approach contrasts multi-scale features based on ground-truth and
cross-predicted labels, in order to extract robust feature representations that
reflect intra- and inter-slice relationships across the whole dataset. To
tackle class imbalance, we take into account the prevalence of each class to
guide contrastive learning and ensure that features adequately capture
infrequent classes. Extensive experiments on two multi-structure medical
segmentation datasets demonstrate the effectiveness of MCSC. It not only
outperforms state-of-the-art semi-supervised methods by more than 3.0% in Dice,
but also greatly reduces the performance gap with fully supervised methods.
