Domain-invariant Mixed-domain Semi-supervised Medical Image Segmentation with Clustered Maximum Mean Discrepancy Alignment

• URL: http://arxiv.org/abs/2601.16954v1
• Date: Fri, 23 Jan 2026 18:23:03 GMT
• Title: Domain-invariant Mixed-domain Semi-supervised Medical Image Segmentation with Clustered Maximum Mean Discrepancy Alignment
• Authors: Ba-Thinh Lam, Thanh-Huy Nguyen, Hoang-Thien Nguyen, Quang-Khai Bui-Tran, Nguyen Lan Vi Vu, Phat K. Huynh, Ulas Bagci, Min Xu,
• Abstract summary: We propose a domain-invariant mixed-domain semi-supervised segmentation framework.<n>A Copy-Paste Mechanism (CPM) augments the training set by transferring informative regions across domains.<n>A Cluster Maximum Mean Discrepancy (CMMD) block clusters unlabeled features and aligns them with labeled anchors.
• Score: 11.298724831730675
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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