C3MT: Confidence-Calibrated Contrastive Mean Teacher for semi-supervised medical image segmentation.
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| Title: | C3MT: Confidence-Calibrated Contrastive Mean Teacher for semi-supervised medical image segmentation. |
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| Authors: | Wang, Xianmin1 (AUTHOR), Lin, Mingfeng1 (AUTHOR), Li, Jing1,2 (AUTHOR) lijing@gzhu.edu.cn |
| Source: | Computerized Medical Imaging & Graphics. Feb2026, Vol. 128, pN.PAG-N.PAG. 1p. |
| Subjects: | Image segmentation, Feature extraction, Supervised learning, Data augmentation |
| Abstract: | Semi-supervised learning is crucial for medical image segmentation due to the scarcity of labeled data. However, existing methods that combine consistency regularization and pseudo-labeling often suffer from inadequate feature representation, suboptimal subnetwork disagreement, and noisy pseudo-labels. To address these limitations, this paper proposed a novel C onfidence- C alibrated C ontrastive M ean T eacher (C3MT) framework. First, C3MT introduces a Contrastive Learning-based co-training strategy, where an adaptive disagreement adjustment mechanism dynamically regulates the divergence between student models. This not only preserves representation diversity but also stabilizes the training process. Second, C3MT introduces a Confidence-Calibrated and Category-Aligned uncertainty-guided region mixing strategy. The confidence-calibrated mechanism filters out unreliable pseudo-labels, whereas the category-aligned design restricts region swapping to patches of the same semantic category, preserving anatomical coherence and preventing semantic inconsistency in the mixed samples. Together, these components significantly enhance feature representation, training stability, and segmentation quality, especially in challenging low-annotation scenarios. Extensive experiments on ACDC, Synapse, and LA datasets show that C3MT consistently outperforms recent state-of-the-art methods. For example, on the ACDC dataset with 20% labeled data, C3MT achieves up to a 4.3% improvement in average Dice score and a reduction in HD95 of more than 1.0 mm compared with strong baselines. The implementation is publicly available at https://github.com/l1654485/C3MT. [Display omitted] • Proposes C3MT: a Confidence-Calibrated contrastive mean teacher framework. • Contrastive Learning-based co-training stabilizes training and diversifies features. • Confidence-Calibrated, Category-Aligned UMIX generates reliable training samples. • Achieves superior segmentation on ACDC, Synapse, and LA under low labels. [ABSTRACT FROM AUTHOR] |
| Copyright of Computerized Medical Imaging & Graphics is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 191663684 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: C3MT: Confidence-Calibrated Contrastive Mean Teacher for semi-supervised medical image segmentation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Xianmin%22">Wang, Xianmin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Mingfeng%22">Lin, Mingfeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Jing%22">Li, Jing</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> lijing@gzhu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computerized+Medical+Imaging+%26+Graphics%22">Computerized Medical Imaging & Graphics</searchLink>. Feb2026, Vol. 128, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Supervised+learning%22">Supervised learning</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Semi-supervised learning is crucial for medical image segmentation due to the scarcity of labeled data. However, existing methods that combine consistency regularization and pseudo-labeling often suffer from inadequate feature representation, suboptimal subnetwork disagreement, and noisy pseudo-labels. To address these limitations, this paper proposed a novel C onfidence- C alibrated C ontrastive M ean T eacher (C3MT) framework. First, C3MT introduces a Contrastive Learning-based co-training strategy, where an adaptive disagreement adjustment mechanism dynamically regulates the divergence between student models. This not only preserves representation diversity but also stabilizes the training process. Second, C3MT introduces a Confidence-Calibrated and Category-Aligned uncertainty-guided region mixing strategy. The confidence-calibrated mechanism filters out unreliable pseudo-labels, whereas the category-aligned design restricts region swapping to patches of the same semantic category, preserving anatomical coherence and preventing semantic inconsistency in the mixed samples. Together, these components significantly enhance feature representation, training stability, and segmentation quality, especially in challenging low-annotation scenarios. Extensive experiments on ACDC, Synapse, and LA datasets show that C3MT consistently outperforms recent state-of-the-art methods. For example, on the ACDC dataset with 20% labeled data, C3MT achieves up to a 4.3% improvement in average Dice score and a reduction in HD95 of more than 1.0 mm compared with strong baselines. The implementation is publicly available at https://github.com/l1654485/C3MT. [Display omitted] • Proposes C3MT: a Confidence-Calibrated contrastive mean teacher framework. • Contrastive Learning-based co-training stabilizes training and diversifies features. • Confidence-Calibrated, Category-Aligned UMIX generates reliable training samples. • Achieves superior segmentation on ACDC, Synapse, and LA under low labels. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computerized Medical Imaging & Graphics is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.compmedimag.2026.102721 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Image segmentation Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Supervised learning Type: general – SubjectFull: Data augmentation Type: general Titles: – TitleFull: C3MT: Confidence-Calibrated Contrastive Mean Teacher for semi-supervised medical image segmentation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Xianmin – PersonEntity: Name: NameFull: Lin, Mingfeng – PersonEntity: Name: NameFull: Li, Jing IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 08956111 Numbering: – Type: volume Value: 128 Titles: – TitleFull: Computerized Medical Imaging & Graphics Type: main |
| ResultId | 1 |
