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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Veröffentlicht in:International journal of imaging systems and technology Jg. 34; H. 3
Hauptverfasser: Yuan, Enze, Zhao, Bin, Qin, Xiao, Ding, Shuxue
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken, USA John Wiley & Sons, Inc 01.05.2024
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Schlagworte:
Consistency
consistency learning
Decoders
Image segmentation
medical image segmentation
Medical imaging
multidimensional perturbation
Perturbation
Semi-supervised learning
Supervised learning
ISSN:0899-9457, 1098-1098
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0899-9457
1098-1098
DOI:10.1002/ima.23095