Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation

arXiv:2403.03512v1 Announce Type: new
Abstract: Semi-supervised learning is a sound measure to relieve the strict demand of abundant annotated datasets, especially for challenging multi-organ segmentation . However, most existing SSL methods predict pixels in a single image independently, ignoring the relations among images and categories. In this paper, we propose a two-stage Dual Contrastive Learning Network for semi-supervised MoS, which utilizes global and local contrastive learning to strengthen the relations among images and classes. Concretely, in Stage 1, we develop …

abstract arxiv cs.cv datasets demand however image images network paper pixels relations segmentation semi-supervised semi-supervised learning sound ssl supervised learning type