Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation

The author proposes a Dual Contrastive Learning Network (DCL-Net) for semi-supervised multi-organ segmentation, incorporating global and local contrastive learning to enhance feature representations. The method demonstrates superior performance in experiments on medical image datasets.