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

本文介绍了一种自我监督学习方法 —— Preservational Contrastive Representation Learning (PCRL)，通过引入Preservational Learning来重建不同的图像空间，并与对比损失联合训练自监督医学表示方法。在5个分类/分割任务中，PCRL在预训练-微调协议下提供了非常有竞争力的结果，优于自我监督和有监督方法。

通过重建多样化场景来改进自监督医学图像模型的保存学习