Contrastive learning, which is a powerful technique for learning image-level representations from unlabeled data, leads a promising direction to dealing with the dilemma between large-scale pre-training and limited labeled data. However, most existing contrastive learning strategies are designed mainly for downstream tasks of natural images, therefore they are sub-optimal and even worse than learning from scratch when directly applied to medical images whose downstream tasks are usually segmentation. In this work, we propose a novel asymmetric contrastive learning framework named JCL for medical image segmentation with self-supervised pre-training. Specifically, (1) A novel asymmetric contrastive learning strategy is proposed to pre-train both encoder and decoder simultaneously in one-stage to provide better initialization for segmentation models. (2) A multi-level contrastive loss is designed to take the correspondence among feature-level, image-level and pixel-level projections, respectively into account to make sure multi-level representations can be learned by the encoder and decoder during pre-training. (3) Experiments on multiple medical image datasets indicate our JCL framework outperforms existing SOTA contrastive learning strategies.

翻译：对比学习是一种从无标签数据中学习图像级表征的强大技术，为解决大规模预训练与有限标注数据之间的矛盾提供了有前景的途径。然而，现有对比学习策略主要面向自然图像的下游任务设计，当直接应用于以分割为主要下游任务的医学图像时，其性能表现欠佳甚至不如从零开始训练。本文提出了一种新颖的非对称对比学习框架JCL，用于实现基于自监督预训练的医学图像分割。具体而言：（1）提出了一种新颖的非对称对比学习策略，可在单阶段同时预训练编码器和解码器，为分割模型提供更优的初始化；（2）设计了一种多层级对比损失函数，分别考虑特征级、图像级和像素级投影之间的对应关系，确保预训练过程中编码器和解码器能够学习多层级表征；（3）在多个医学图像数据集上的实验表明，我们提出的JCL框架优于现有最先进的对比学习策略。