ACL-Net: Semi-supervised Polyp Segmentation via Affinity Contrastive Learning

Automatic polyp segmentation from colonoscopy images is an essential prerequisite for the development of computer-assisted therapy. However, the complex semantic information and the blurred edges of polyps make segmentation extremely difficult. In this paper, we propose a novel semi-supervised polyp segmentation framework using affinity contrastive learning (ACL-Net), which is implemented between student and teacher networks to consistently refine the pseudo-labels for semi-supervised polyp segmentation. By aligning the affinity maps between the two branches, a better polyp region activation can be obtained to fully exploit the appearance-level context encoded in the feature maps, thereby improving the capability of capturing not only global localization and shape context, but also the local textural and boundary details. By utilizing the rich inter-image affinity context and establishing a global affinity context based on the memory bank, a cross-image affinity aggregation (CAA) module is also implemented to further refine the affinity aggregation between the two branches. By continuously and adaptively refining pseudo-labels with optimized affinity, we can improve the semi-supervised polyp segmentation based on the mutually reinforced knowledge interaction among contrastive learning and consistency learning iterations. Extensive experiments on five benchmark datasets, including Kvasir-SEG, CVC-ClinicDB, CVC-300, ColonDB and ETIS, demonstrate the effectiveness and superiority of our method. Codes are available at https://github.com/xiewende/ACL-Net.