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Semi-supervised unpaired medical image segmentation through task-affinity consistency
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Chen, Jingkun, Zhang, Jianguo, Debattista, Kurt and Han, Jungong (2023) Semi-supervised unpaired medical image segmentation through task-affinity consistency. IEEE Transactions on Medical Imaging, 42 (3). pp. 594-605. doi:10.1109/TMI.2022.3213372 ISSN 0278-0062.
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WRAP-semi-supervised-unpaired-medical-image-segmentation-through-task-affinity-consistency-2022.pdf - Accepted Version - Requires a PDF viewer. Download (4Mb) | Preview |
Official URL: http://dx.doi.org/10.1109/TMI.2022.3213372
Abstract
Deep learning-based semi-supervised learning (SSL) algorithms are promising in reducing the cost of manual annotation of clinicians by using unlabelled data, when developing medical image segmentation tools. However, to date, most existing semi-supervised learning (SSL) algorithms treat the labelled images and unlabelled images separately and ignore the explicit connection between them; this disregards essential shared information and thus hinders further performance improvements. To mine the shared information between the labelled and unlabelled images, we introduce a class-specific representation extraction approach, in which a task-affinity module is specifically designed for representation extraction. We further cast the representation into two different views of feature maps; one is focusing on low-level context, while the other concentrates on structural information. The two views of feature maps are incorporated into the task-affinity module, which then extracts the class-specific representations to aid the knowledge transfer from the labelled images to the unlabelled images. In particular, a task-affinity consistency loss between the labelled images and unlabelled images based on the multi-scale class-specific representations is formulated, leading to a significant performance improvement. Experimental results on three datasets show that our method consistently outperforms existing state-of-the-art methods. Our findings highlight the potential of consistency between class-specific knowledge for semi-supervised medical image segmentation. The code and models are to be made publicly available at https://github.com/jingkunchen/TAC.
Item Type: | Journal Article | |||||||||
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Subjects: | Q Science > Q Science (General) R Medicine > RC Internal medicine T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | |||||||||
Library of Congress Subject Headings (LCSH): | Diagnostic imaging -- Data processing, Deep learning (Machine learning), Image segmentation | |||||||||
Journal or Publication Title: | IEEE Transactions on Medical Imaging | |||||||||
Publisher: | IEEE | |||||||||
ISSN: | 0278-0062 | |||||||||
Official Date: | March 2023 | |||||||||
Dates: |
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Volume: | 42 | |||||||||
Number: | 3 | |||||||||
Page Range: | pp. 594-605 | |||||||||
DOI: | 10.1109/TMI.2022.3213372 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Reuse Statement (publisher, data, author rights): | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||
Date of first compliant deposit: | 14 October 2022 | |||||||||
Date of first compliant Open Access: | 14 October 2022 | |||||||||
RIOXX Funder/Project Grant: |
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