Beyond Random Sampling: Distribution-Aware Alignment for Semi-Supervised Medical Image Segmentation

• URL: http://arxiv.org/abs/2607.04249v1
• Date: Sun, 05 Jul 2026 11:59:47 GMT
• Title: Beyond Random Sampling: Distribution-Aware Alignment for Semi-Supervised Medical Image Segmentation
• Abstract summary: Semi-supervised medical image segmentation (SSMIS) offers a cost-effective solution but operates under the assumption of independent and identically distributed (i.i.d.) data.<n>This strategy suffers from severe representation bias in low-data regimes, failing to capture the heterogeneous medical data manifold.<n>We propose a highly data-efficient framework driven by distribution alignment.
• Score: 14.677758755539337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Precise medical image segmentation is crucial for clinical diagnosis and treatment planning, yet relies heavily on expensive expert annotations. Semi-supervised medical image segmentation (SSMIS) offers a cost-effective solution but typically operates under the assumption of independent and identically distributed (i.i.d.) data, defaulting to random sampling. While statistically valid at scale, this strategy suffers from severe representation bias in low-data regimes, failing to capture the heterogeneous medical data manifold. To address this, we propose a highly data-efficient framework driven by distribution alignment. First, we introduce an offline Distribution-Aware Sample Selection strategy. By leveraging Vision Foundation Models (VFMs) and our designed Density-K-Center algorithm, we explicitly identify representative structural anchors, establishing a more representative labeled domain. Second, to bridge the remaining distribution gap, we propose the Memory-guided Copy-Paste (MCP) module. Tailored for the inherent class imbalance in medical scans, MCP leverages a semantic memory mechanism to retrieve historically consistent priors for cross-domain alignment, encouraging semantic consistency. Coupled with an easy-to-hard progressive schedule, this framework effectively mitigates early-stage pseudo-label noise. Extensive experiments on six diverse 2D and 3D datasets demonstrate strong segmentation performance, particularly in extremely low-labeled scenarios (\eg, 1/16 ratio).

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