  Medical image segmentation has been widely recognized as a pivot procedure
for clinical diagnosis, analysis, and treatment planning. However, the
laborious and expensive annotation process lags down the speed of further
advances. Contrastive learning-based weight pre-training provides an
alternative by leveraging unlabeled data to learn a good representation. In
this paper, we investigate how contrastive learning benefits the general
supervised medical segmentation tasks. To this end, patch-dragsaw contrastive
regularization (PDCR) is proposed to perform patch-level tugging and repulsing
with the extent controlled by a continuous affinity score. And a new structure
dubbed uncertainty-aware feature selection block (UAFS) is designed to perform
the feature selection process, which can handle the learning target shift
caused by minority features with high uncertainty. By plugging the proposed 2
modules into the existing segmentation architecture, we achieve
state-of-the-art results across 8 public datasets from 6 domains. Newly
designed modules further decrease the amount of training data to a quarter
while achieving comparable, if not better, performances. From this perspective,
we take the opposite direction of the original self/un-supervised contrastive
learning by further excavating information contained within the label.
