VCDP：用于半监督医学图像分割的变异条件分布代理学习
VCDP: Variation-Conditioned Distributional Proxy Learning for Semi-Supervised Medical Image Segmentation
AI总结：
研究半监督3D医学图像分割问题，提出VCDP方法，通过可学习高斯分布和变异原型表示类语义及模式，用统一分数融合相似性与聚合，改进了多器官分割基线，尤其对复杂器官分割效果好。
摘要：
半监督3D医学图像分割通过利用未标记体积减少了对密集体素级注释的需求。现有方法虽提高了预测级鲁棒性，但在特征空间组织上不足。为此提出变异条件分布代理学习（VCDP），它为半监督3D医学图像分割提供即插即用的仅训练正则化模块。VCDP用可学习的高斯分布表示类语义，用多个变异原型表示细粒度类内模式，还制定统一分数融合分布相似性和软变异聚合。实验表明VCDP改进了多数基线，尤其对小的、模糊的和高度可变的器官。
英文摘要：
Semi-supervised 3D medical image segmentation reduces the need for dense voxel-level annotations by exploiting unlabeled volumes. Although existing methods such as consistency regularization, pseudo-labeling, and co-training improve prediction-level robustness, they often provide insufficient feature-space organization for anatomically complex structures, especially small organs and ambiguous boundary regions with large intra-class variations. To address this issue, we propose Variation-Conditioned Distributional Proxy Learning (VCDP), a plug-and-play training-only regularization module for semi-supervised 3D medical image segmentation. VCDP represents each class with a learnable Gaussian distribution for shared class semantics and multiple variation prototypes for fine-grained intra-class patterns. A unified variation-conditioned compatibility score is further formulated to fuse distributional similarity and soft variation aggregation, guiding voxel embeddings to align with both global organ identity and local anatomical variations. VCDP is attached to decoder features during training and removed during inference, introducing no additional inference cost. Experiments on multi-organ segmentation benchmarks show that VCDP improves most evaluated baselines, particularly for small, ambiguous, and highly variable organs. Our anonymous code is released at https://anonymous.4open.science/r/VCDP_code-41ED.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
