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|a 10.1109/TIP.2025.3536208
|2 doi
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Huang, Senlong
|e verfasserin
|4 aut
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| 245 |
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|a Rethinking Copy-Paste for Consistency Learning in Medical Image Segmentation
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|c 2025
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 10.05.2025
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|a Date Revised 10.05.2025
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Semi-supervised learning based on consistency learning offers significant promise for enhancing medical image segmentation. Current approaches use copy-paste as an effective data perturbation technique to facilitate weak-to-strong consistency learning. However, these techniques often lead to a decrease in the accuracy of synthetic labels corresponding to the synthetic data and introduce excessive perturbations to the distribution of the training data. Such over-perturbation causes the data distribution to stray from its true distribution, thereby impairing the model's generalization capabilities as it learns the decision boundaries. We propose a weak-to-strong consistency learning framework that integrally addresses these issues with two primary designs: 1) it emphasizes the use of highly reliable data to enhance the quality of labels in synthetic datasets through cross-copy-pasting between labeled and unlabeled datasets; 2) it employs uncertainty estimation and foreground region constraints to meticulously filter the regions for copy-pasting, thus the copy-paste technique implemented introduces a beneficial perturbation to the training data distribution. Our framework expands the copy-paste method by addressing its inherent limitations, and amplifying the potential of data perturbations for consistency learning. We extensively validated our model using six publicly available medical image segmentation datasets across different diagnostic tasks, including the segmentation of cardiac structures, prostate structures, brain structures, skin lesions, and gastrointestinal polyps. The results demonstrate that our method significantly outperforms state-of-the-art models. For instance, on the PROMISE12 dataset for the prostate structure segmentation task, using only 10% labeled data, our method achieves a 15.31% higher Dice score compared to the baseline models. Our experimental code will be made publicly available at https://github.com/slhuang24/RCP4CL
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|a Journal Article
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| 700 |
1 |
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|a Ge, Yongxin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Dongfang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hong, Mingjian
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhao, Junhan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Loui, Alexander C
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 34(2025) vom: 04., Seite 1060-1074
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnas
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| 773 |
1 |
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|g volume:34
|g year:2025
|g day:04
|g pages:1060-1074
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2025.3536208
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