  A key requirement for the success of supervised deep learning is a large
labeled dataset - a condition that is difficult to meet in medical image
analysis. Self-supervised learning (SSL) can help in this regard by providing a
strategy to pre-train a neural network with unlabeled data, followed by
fine-tuning for a downstream task with limited annotations. Contrastive
learning, a particular variant of SSL, is a powerful technique for learning
image-level representations. In this work, we propose strategies for extending
the contrastive learning framework for segmentation of volumetric medical
images in the semi-supervised setting with limited annotations, by leveraging
domain-specific and problem-specific cues. Specifically, we propose (1) novel
contrasting strategies that leverage structural similarity across volumetric
medical images (domain-specific cue) and (2) a local version of the contrastive
loss to learn distinctive representations of local regions that are useful for
per-pixel segmentation (problem-specific cue). We carry out an extensive
evaluation on three Magnetic Resonance Imaging (MRI) datasets. In the limited
annotation setting, the proposed method yields substantial improvements
compared to other self-supervision and semi-supervised learning techniques.
When combined with a simple data augmentation technique, the proposed method
reaches within 8% of benchmark performance using only two labeled MRI volumes
for training, corresponding to only 4% (for ACDC) of the training data used to
train the benchmark. The code is made public at
https://github.com/krishnabits001/domain_specific_cl.
