Multidimensional perturbed consistency learning for semi‐supervised medical image segmentation
In this article, we propose a novel multidimensional perturbed consistency network (MPCNet) for more accurate semi‐supervised medical image segmentation. Specifically, we develop a multidimensional perturbation by considering the noise itself, the target object and the overall spatial architecture....
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| Published in: | International journal of imaging systems and technology Vol. 34; no. 3 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Hoboken, USA
John Wiley & Sons, Inc
01.05.2024
Wiley Subscription Services, Inc |
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| ISSN: | 0899-9457, 1098-1098 |
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| Abstract | In this article, we propose a novel multidimensional perturbed consistency network (MPCNet) for more accurate semi‐supervised medical image segmentation. Specifically, we develop a multidimensional perturbation by considering the noise itself, the target object and the overall spatial architecture. This type of perturbation can disrupt the propagation process of features over a wide range, enabling the model to learn the distribution of comprehensive data. In addition, we design a shared encoder to extract multi‐scale features. After subjecting these features to multidimensional perturbation, a consistency constraint is applied between different results output by three independent decoders. This constraint aims to minimize the statistical differences between these results and effectively leverage unlabeled data. Experimental results on the public LA, Pancreas‐CT and ACDC datasets demonstrate that our method outperforms recent SOTA semi‐supervised learning methods in terms of various metrics. Our code is released publicly at https://github.com/yuanenze123/MPC-Net. |
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| AbstractList | In this article, we propose a novel multidimensional perturbed consistency network (MPCNet) for more accurate semi‐supervised medical image segmentation. Specifically, we develop a multidimensional perturbation by considering the noise itself, the target object and the overall spatial architecture. This type of perturbation can disrupt the propagation process of abstract features over a wide range, enabling the model to learn the distribution of comprehensive data. In addition, we design a shared encoder to extract multi‐scale features. After subjecting these features to multidimensional perturbation, a consistency constraint is applied between different results output by three independent decoders. This constraint aims to minimize the statistical differences between these results and effectively leverage unlabeled data. Experimental results on the public LA, Pancreas‐CT and ACDC datasets demonstrate that our method outperforms recent SOTA semi‐supervised learning methods in terms of various metrics. Our code is released publicly at https://github.com/yuanenze123/MPC-Net. In this article, we propose a novel multidimensional perturbed consistency network (MPCNet) for more accurate semi‐supervised medical image segmentation. Specifically, we develop a multidimensional perturbation by considering the noise itself, the target object and the overall spatial architecture. This type of perturbation can disrupt the propagation process of features over a wide range, enabling the model to learn the distribution of comprehensive data. In addition, we design a shared encoder to extract multi‐scale features. After subjecting these features to multidimensional perturbation, a consistency constraint is applied between different results output by three independent decoders. This constraint aims to minimize the statistical differences between these results and effectively leverage unlabeled data. Experimental results on the public LA, Pancreas‐CT and ACDC datasets demonstrate that our method outperforms recent SOTA semi‐supervised learning methods in terms of various metrics. Our code is released publicly at https://github.com/yuanenze123/MPC-Net. In this article, we propose a novel multidimensional perturbed consistency network (MPCNet) for more accurate semi‐supervised medical image segmentation. Specifically, we develop a multidimensional perturbation by considering the noise itself, the target object and the overall spatial architecture. This type of perturbation can disrupt the propagation process of abstract features over a wide range, enabling the model to learn the distribution of comprehensive data. In addition, we design a shared encoder to extract multi‐scale features. After subjecting these features to multidimensional perturbation, a consistency constraint is applied between different results output by three independent decoders. This constraint aims to minimize the statistical differences between these results and effectively leverage unlabeled data. Experimental results on the public LA, Pancreas‐CT and ACDC datasets demonstrate that our method outperforms recent SOTA semi‐supervised learning methods in terms of various metrics. Our code is released publicly at https://github.com/yuanenze123/MPC-Net . |
| Author | Qin, Xiao Ding, Shuxue Zhao, Bin Yuan, Enze |
| Author_xml | – sequence: 1 givenname: Enze surname: Yuan fullname: Yuan, Enze organization: Guangxi Colleges and Universities Key Laboratory of AI Algorithm Engineering – sequence: 2 givenname: Bin surname: Zhao fullname: Zhao, Bin email: zhaobin@guet.edu.cn organization: Guilin University of Electronic Technology – sequence: 3 givenname: Xiao surname: Qin fullname: Qin, Xiao organization: Guangxi Academy of Science – sequence: 4 givenname: Shuxue surname: Ding fullname: Ding, Shuxue email: sding@guet.edu.cn organization: Guangxi Colleges and Universities Key Laboratory of AI Algorithm Engineering |
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| Cites_doi | 10.1007/978-3-030-59710-8_55 10.1007/978-3-030-32245-8_67 10.1016/j.compmedimag.2022.102092 10.1109/TNNLS.2012.2186825 10.1007/978-3-031-16434-7_1 10.1109/TMI.2018.2845918 10.1109/CVPR.2015.7298965 10.1016/j.media.2022.102517 10.2174/157340561101150423103441 10.1007/978-3-030-59710-8_54 10.1109/TBDATA.2023.3258643 10.1109/CVPR42600.2020.01269 10.1038/s41592-020-01008-z 10.1016/j.media.2020.101832 10.1109/ISBI52829.2022.9761666 10.1016/j.compbiomed.2022.105729 10.1109/CVPR46437.2021.00264 10.1109/ICCV.2019.01077 10.1109/CVPR.2015.7298664 10.1109/CVPR.2018.00454 10.1109/3DV.2016.79 10.1016/j.media.2022.102530 10.1109/TMI.2018.2837502 10.1007/s10278-013-9622-7 |
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| SubjectTerms | Consistency consistency learning Decoders Image segmentation medical image segmentation Medical imaging multidimensional perturbation Perturbation Semi-supervised learning Supervised learning |
| Title | Multidimensional perturbed consistency learning for semi‐supervised medical image segmentation |
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