Semi-supervised medical image segmentation via hard positives oriented contrastive learning

Cheng Tang, Xinyi Zeng, Luping Zhou, Qizheng Zhou, Peng Wang, Xi Wu, Hongping Ren, Jiliu Zhou, Yan Wang

Abstract: Highlights•The paper proposes a hard positives oriented contrastive learning strategy for semi-supervised medical image segmentation.•The HPC strategy is constructed from two levels: an unsupervised image-level HPC and a supervised pixel-level HPC.•The pixel-level HPC is implemented in a region-based manner to save memory and deliver more multi-granularity information.•The paper inserts several feature swap modules into the pre-trained decoder to encourage robust segmentation predictions.•The proposed framework outperforms the state-of-the-art methods on two public clinical datasets.