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A key requirement for the success of supervised deep learning is a largelabeled dataset - a condition that is difficult to meet in medical imageanalysis. Self-supervised learning (SSL) can help in this regard by providing astrategy to pre-train a neural network with unlabeled data, followed byfine-tuning for a downstream task with limited annotations. Contrastivelearning, a particular variant of SSL, is a powerful technique for learningimage-level representations. In this work, we propose strategies for extendingthe contrastive learning framework for segmentation of volumetric medicalimages in the semi-supervised setting with limited annotations, by leveragingdomain-specific and problem-specific cues. Specifically, we propose (1) novelcontrasting strategies that leverage structural similarity across volumetricmedical images (domain-specific cue) and (2) a local version of the contrastiveloss to learn distinctive representations of local regions that are useful forper-pixel segmentation (problem-specific cue). We carry out an extensiveevaluation on three Magnetic Resonance Imaging (MRI) datasets. In the limitedannotation setting, the proposed method yields substantial improvementscompared to other self-supervision and semi-supervised learning techniques.When combined with a simple data augmentation technique, the proposed methodreaches within 8% of benchmark performance using only two labeled MRI volumesfor training, corresponding to only 4% (for ACDC) of the training data used totrain the benchmark. The code is made public athttps://github.com/krishnabits001/domain_specific_cl.

Contrastive learning of global and local features for medical image segmentation with limited annotations