Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917660802940928 |
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| author | Wen, Lu Feng, Zhenghao Hou, Yun Wang, Peng Wu, Xi Zhou, Jiliu Wang, Yan |
| author_facet | Wen, Lu Feng, Zhenghao Hou, Yun Wang, Peng Wu, Xi Zhou, Jiliu Wang, Yan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_03512 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation Wen, Lu Feng, Zhenghao Hou, Yun Wang, Peng Wu, Xi Zhou, Jiliu Wang, Yan Computer Vision and Pattern Recognition 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. |
| title | Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.03512 |