Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation
<|reference_start|>Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation: 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 a similarity-guided global contrastive learning to explore the implicit continuity and similarity among images and learn global context. Then, in Stage 2, we present an organ-aware local contrastive learning to further attract the class representations. To ease the computation burden, we introduce a mask center computation algorithm to compress the category representations for local contrastive learning. Experiments conducted on the public 2017 ACDC dataset and an in-house RC-OARs dataset has demonstrated the superior performance of our method.<|reference_end|>
@article{wen2024dcl-net:,
    title={Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised
  Multi-Organ Segmentation},
    author={Lu Wen, Zhenghao Feng, Yun Hou, Peng Wang, Xi Wu, Jiliu Zhou, Yan Wang},
    journal={arXiv preprint arXiv:2403.03512},
    year={2024},
    doi={10.1109/ICASSP48485.2024.10447495},
    archivePrefix={arXiv},
    eprint={2403.03512},
    primaryClass={cs.CV}
}