  The success of deep learning heavily depends on the availability of large
labeled training sets. However, it is hard to get large labeled datasets in
medical image domain because of the strict privacy concern and costly labeling
efforts. Contrastive learning, an unsupervised learning technique, has been
proved powerful in learning image-level representations from unlabeled data.
The learned encoder can then be transferred or fine-tuned to improve the
performance of downstream tasks with limited labels. A critical step in
contrastive learning is the generation of contrastive data pairs, which is
relatively simple for natural image classification but quite challenging for
medical image segmentation due to the existence of the same tissue or organ
across the dataset. As a result, when applied to medical image segmentation,
most state-of-the-art contrastive learning frameworks inevitably introduce a
lot of false-negative pairs and result in degraded segmentation quality. To
address this issue, we propose a novel positional contrastive learning (PCL)
framework to generate contrastive data pairs by leveraging the position
information in volumetric medical images. Experimental results on CT and MRI
datasets demonstrate that the proposed PCL method can substantially improve the
segmentation performance compared to existing methods in both semi-supervised
setting and transfer learning setting.
