Dual Diversity and Pseudo‐Label Correction Learning for Semi‐Supervised Medical Image Segmentation
ABSTRACT Semi‐supervised medical image segmentation has recently gained increasing research attention as it can reduce the need for large‐scale annotated data. Current mainstream methods usually adopt two sub‐networks and encourage the two models to make consistent predictions for the same segmentat...
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| Vydáno v: | International journal of imaging systems and technology Ročník 35; číslo 5 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Hoboken, USA
John Wiley & Sons, Inc
01.09.2025
Wiley Subscription Services, Inc |
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| ISSN: | 0899-9457, 1098-1098 |
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| Abstract | ABSTRACT
Semi‐supervised medical image segmentation has recently gained increasing research attention as it can reduce the need for large‐scale annotated data. Current mainstream methods usually adopt two sub‐networks and encourage the two models to make consistent predictions for the same segmentation task through consistency regularization. However, the scarcity of medical samples reduces the effectiveness of consistency constraints, and this problem may be further exacerbated by the influence of noisy pseudo‐labels. In this work, we propose a novel co‐training framework based on dual diversity and pseudo‐label correction learning (DDPCL) to address these challenges. Specifically, firstly, we design a dual diversity learning strategy, in which data diversity fully mines the potential information of limited training samples through the CutMix operation, and feature diversity promotes the model to learn complementary feature representations by minimizing the similarity between the features extracted by the two sub‐networks. Secondly, we propose a pseudo‐label correction learning strategy, which regards the inconsistent region where the pseudo‐labels predicted by the two sub‐networks are different as potential bias regions, and guides the models to correct the bias in these regions. Extensive experiments on three public datasets (ACDC, LA and Pancreas‐NIH datasets) validate that the proposed method outperforms the state‐of‐the‐art semi‐supervised medical image segmentation. The code is available at http://github.com/ddd0420/ddpcl. |
|---|---|
| AbstractList | ABSTRACT
Semi‐supervised medical image segmentation has recently gained increasing research attention as it can reduce the need for large‐scale annotated data. Current mainstream methods usually adopt two sub‐networks and encourage the two models to make consistent predictions for the same segmentation task through consistency regularization. However, the scarcity of medical samples reduces the effectiveness of consistency constraints, and this problem may be further exacerbated by the influence of noisy pseudo‐labels. In this work, we propose a novel co‐training framework based on dual diversity and pseudo‐label correction learning (DDPCL) to address these challenges. Specifically, firstly, we design a dual diversity learning strategy, in which data diversity fully mines the potential information of limited training samples through the CutMix operation, and feature diversity promotes the model to learn complementary feature representations by minimizing the similarity between the features extracted by the two sub‐networks. Secondly, we propose a pseudo‐label correction learning strategy, which regards the inconsistent region where the pseudo‐labels predicted by the two sub‐networks are different as potential bias regions, and guides the models to correct the bias in these regions. Extensive experiments on three public datasets (ACDC, LA and Pancreas‐NIH datasets) validate that the proposed method outperforms the state‐of‐the‐art semi‐supervised medical image segmentation. The code is available at http://github.com/ddd0420/ddpcl. Semi‐supervised medical image segmentation has recently gained increasing research attention as it can reduce the need for large‐scale annotated data. Current mainstream methods usually adopt two sub‐networks and encourage the two models to make consistent predictions for the same segmentation task through consistency regularization. However, the scarcity of medical samples reduces the effectiveness of consistency constraints, and this problem may be further exacerbated by the influence of noisy pseudo‐labels. In this work, we propose a novel co‐training framework based on dual diversity and pseudo‐label correction learning (DDPCL) to address these challenges. Specifically, firstly, we design a dual diversity learning strategy, in which data diversity fully mines the potential information of limited training samples through the CutMix operation, and feature diversity promotes the model to learn complementary feature representations by minimizing the similarity between the features extracted by the two sub‐networks. Secondly, we propose a pseudo‐label correction learning strategy, which regards the inconsistent region where the pseudo‐labels predicted by the two sub‐networks are different as potential bias regions, and guides the models to correct the bias in these regions. Extensive experiments on three public datasets (ACDC, LA and Pancreas‐NIH datasets) validate that the proposed method outperforms the state‐of‐the‐art semi‐supervised medical image segmentation. The code is available at http://github.com/ddd0420/ddpcl. |
| Author | Xu, Jinming Wu, Rui Du, Guangxing Zeng, Xiang Xiong, Shengwu |
| Author_xml | – sequence: 1 givenname: Guangxing orcidid: 0000-0002-5164-619X surname: Du fullname: Du, Guangxing organization: Wuhan University of Technology – sequence: 2 givenname: Rui surname: Wu fullname: Wu, Rui organization: Wuhan University of Technology – sequence: 3 givenname: Jinming surname: Xu fullname: Xu, Jinming organization: Wuhan University of Technology – sequence: 4 givenname: Xiang surname: Zeng fullname: Zeng, Xiang organization: Wuhan University of Technology – sequence: 5 givenname: Shengwu surname: Xiong fullname: Xiong, Shengwu email: xiongsw@whut.edu.cn organization: Wuhan College |
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| Cites_doi | 10.1109/TMI.2021.3123461 10.1016/j.patcog.2024.111028 10.1038/s42256-023-00682-w 10.1016/j.media.2024.103329 10.1016/j.media.2023.102880 10.1016/j.patcog.2020.107269 10.3390/app12178662 10.1109/TMI.2018.2837502 10.1145/3664647.3680699 10.1016/j.media.2020.101832 10.24963/ijcai.2023/467 10.1007/978-3-319-24553-9_68 10.1609/aaai.v35i10.17066 10.1109/TMI.2025.3556310 10.1109/CVPR46437.2021.00264 10.1109/CVPR52734.2025.00488 10.1016/j.media.2024.103302 10.1007/s11263-024-02328-9 10.1016/j.neucom.2025.129818 10.1109/CVPR52729.2023.01502 10.1109/TMI.2025.3540211 10.1007/s13534-025-00481-9 10.1016/j.media.2024.103111 |
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| Notes | Funding This work was in part supported by the National Key Research and Development Program of China (Grant No. 2022ZD0160604) and NSFC (Grant No. 62176194), and the Key Research and Development Program of Hubei Province (Grant No. 2024BAB030). ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
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Semi‐supervised medical image segmentation has recently gained increasing research attention as it can reduce the need for large‐scale annotated data.... Semi‐supervised medical image segmentation has recently gained increasing research attention as it can reduce the need for large‐scale annotated data. Current... |
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| SubjectTerms | Bias co‐training Datasets Feature extraction Image segmentation Labels Learning medical image segmentation Medical imaging Networks pseudo‐labeling Regularization semi‐supervised learning |
| Title | Dual Diversity and Pseudo‐Label Correction Learning for Semi‐Supervised Medical Image Segmentation |
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