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Pre-training a recognition model with contrastive learning on a large datasetof unlabeled data has shown great potential to boost the performance of adownstream task, e.g., image classification. However, in domains such asmedical imaging, collecting unlabeled data can be challenging and expensive. Inthis work, we propose to adapt contrastive learning to work with meta-labelannotations, for improving the model's performance in medical imagesegmentation even when no additional unlabeled data is available. Meta-labelssuch as the location of a 2D slice in a 3D MRI scan or the type of device used,often come for free during the acquisition process. We use the meta-labels forpre-training the image encoder as well as to regularize a semi-supervisedtraining, in which a reduced set of annotated data is used for training.Finally, to fully exploit the weak annotations, a self-paced learning approachis used to help the learning and discriminate useful labels from noise. Resultson three different medical image segmentation datasets show that our approach:i) highly boosts the performance of a model trained on a few scans, ii)outperforms previous contrastive and semi-supervised approaches, and iii)reaches close to the performance of a model trained on the full data.

Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels