  Unsupervised pre-training has been proven as an effective approach to boost
various downstream tasks given limited labeled data. Among various methods,
contrastive learning learns a discriminative representation by constructing
positive and negative pairs. However, it is not trivial to build reasonable
pairs for a segmentation task in an unsupervised way. In this work, we propose
a novel unsupervised pre-training framework that avoids the drawback of
contrastive learning. Our framework consists of two principles: unsupervised
over-segmentation as a pre-train task using mutual information maximization and
boundary-aware preserving learning. Experimental results on two benchmark
medical segmentation datasets reveal our method's effectiveness in improving
segmentation performance when few annotated images are available.
