Synergy-Guided Regional Supervision of Pseudo Labels for Semi-Supervised Medical Image Segmentation
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916479016894464 |
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| author | Wang, Tao Zhang, Xinlin Chen, Yuanbin Zhou, Yuanbo Zhao, Longxuan Tan, Tao Tong, Tong |
| author_facet | Wang, Tao Zhang, Xinlin Chen, Yuanbin Zhou, Yuanbo Zhao, Longxuan Tan, Tao Tong, Tong |
| contents | Semi-supervised learning has received considerable attention for its potential to leverage abundant unlabeled data to enhance model robustness. Pseudo labeling is a widely used strategy in semi supervised learning. However, existing methods often suffer from noise contamination, which can undermine model performance. To tackle this challenge, we introduce a novel Synergy-Guided Regional Supervision of Pseudo Labels (SGRS-Net) framework. Built upon the mean teacher network, we employ a Mix Augmentation module to enhance the unlabeled data. By evaluating the synergy before and after augmentation, we strategically partition the pseudo labels into distinct regions. Additionally, we introduce a Region Loss Evaluation module to assess the loss across each delineated area. Extensive experiments conducted on the LA dataset have demonstrated superior performance over state-of-the-art techniques, underscoring the efficiency and practicality of our framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_04493 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Synergy-Guided Regional Supervision of Pseudo Labels for Semi-Supervised Medical Image Segmentation Wang, Tao Zhang, Xinlin Chen, Yuanbin Zhou, Yuanbo Zhao, Longxuan Tan, Tao Tong, Tong Computer Vision and Pattern Recognition Machine Learning Semi-supervised learning has received considerable attention for its potential to leverage abundant unlabeled data to enhance model robustness. Pseudo labeling is a widely used strategy in semi supervised learning. However, existing methods often suffer from noise contamination, which can undermine model performance. To tackle this challenge, we introduce a novel Synergy-Guided Regional Supervision of Pseudo Labels (SGRS-Net) framework. Built upon the mean teacher network, we employ a Mix Augmentation module to enhance the unlabeled data. By evaluating the synergy before and after augmentation, we strategically partition the pseudo labels into distinct regions. Additionally, we introduce a Region Loss Evaluation module to assess the loss across each delineated area. Extensive experiments conducted on the LA dataset have demonstrated superior performance over state-of-the-art techniques, underscoring the efficiency and practicality of our framework. |
| title | Synergy-Guided Regional Supervision of Pseudo Labels for Semi-Supervised Medical Image Segmentation |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2411.04493 |