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Preserving maximal information is one of principles of designingself-supervised learning methodologies. To reach this goal, contrastivelearning adopts an implicit way which is contrasting image pairs. However, webelieve it is not fully optimal to simply use the contrastive estimation forpreservation. Moreover, it is necessary and complemental to introduce anexplicit solution to preserve more information. From this perspective, weintroduce Preservational Learning to reconstruct diverse image contexts inorder to preserve more information in learned representations. Together withthe contrastive loss, we present Preservational Contrastive RepresentationLearning (PCRL) for learning self-supervised medical representations. PCRLprovides very competitive results under the pretraining-finetuning protocol,outperforming both self-supervised and supervised counterparts in 5classification/segmentation tasks substantially.

Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts