Domain-Invariant Mixed-Domain Semi-Supervised Medical Image Segmentation with Clustered Maximum Mean Discrepancy Alignment
Lam, Ba-Thinh Nguyen, Thanh-Huy Nguyen, Hoang-Thien Bui-Tran, Quang-Khai Vu, Nguyen Lan Vi Huynh, Phat K Bagci, Ulas Xu, Min
Lam, Ba-Thinh
Nguyen, Thanh-Huy
Nguyen, Hoang-Thien
Bui-Tran, Quang-Khai
Vu, Nguyen Lan Vi
Huynh, Phat K
Bagci, Ulas
Xu, Min
Supervisor
Department
Computer Vision
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Conference proceeding
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Language
English
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Abstract
Deep learning has shown remarkable progress in medical image semantic segmentation, yet its success heavily depends on large-scale expert annotations and consistent data distributions. In practice, annotations are scarce and images are collected from multiple scanners or centers, leading to mixed-domain settings with unknown domain labels and severe domain gaps. Existing semi-supervised or domain adaptation approaches typically assume either a single domain shift or access to explicit domain indices, which rarely hold in real-world deployment. In this paper, we propose a domain-invariant mixed-domain semi-supervised segmentation framework that jointly enhances data diversity and mitigates domain bias. A Copy-Paste Mechanism (CPM) augments the training set by transferring informative regions across domains, while a Cluster Maximum Mean Discrepancy (CMMD) block clusters unlabeled features and aligns them with labeled anchors via an MMD objective, encouraging domain-invariant representations. Integrated within a teacher–student framework, our method achieves robust and precise segmentation even with very few labeled examples and multiple unknown domain discrepancies. Experiments on Fundus and M&Ms benchmarks demonstrate that our approach consistently surpasses semi-supervised and domain adaptation methods, establishing a potential solution for mixed-domain semi-supervised medical image segmentation.
Citation
B.-T. Lam, T.-H. Nguyen, H.-T. Nguyen, Q.-K. Bui-Tran, N.L.V. Vu, P.K. Huynh , et al., "Domain-Invariant Mixed-Domain Semi-Supervised Medical Image Segmentation with Clustered Maximum Mean Discrepancy Alignment," 2026, pp. 7662-7666.
Source
ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Conference
2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Keywords
46 Information and Computing Sciences, 4603 Computer Vision and Multimedia Computation, 4611 Machine Learning
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Source
2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publisher
IEEE