Authors
Lijuan Yue, Biyuan Li, Jinying Ma, Jiangtao Liu, Xiaofeng Yao
Published in
Computers in biology and medicine. Volume 217. Pages 112024. Oct 31, 2026. Epub Oct 31, 2026.
Abstract
Three-dimensional medical image segmentation plays a critical role in clinical diagnosis and surgical planning. Semi-supervised learning provides an effective way to reduce annotation requirements; however, heterogeneous feature perturbations may induce predictions with different levels of local disagreement, making uniformly weighted consistency supervision sub-optimal. This work presents an Adaptive Consistency Representation Learning (ACRL) framework for semi-supervised 3D medical image segmentation. ACRL constructs heterogeneous feature-perturbed auxiliary views and adaptively modulates the voxel-wise consistency strength according to the disagreement between each auxiliary prediction and the feature-unperturbed main prediction. A Multi-scale Feature Aggregation module and a Bottleneck Attention Module are incorporated to enhance structural representation. Experiments on the LA, Pancreas-CT, and BraTS19 datasets achieve Dice scores of 90.54%, 81.85%, and 87.51%, respectively, under the 10% labeled setting. Across the evaluated settings, ACRL shows competitive overlap accuracy and favorable boundary-distance performance relative to representative conventional and foundation-model-assisted semi-supervised baselines. Controlled ablations further show that reliability-adaptive weighting provides additional benefits over uniformly weighted multi-branch consistency.
PMID:
42966328
Bibliographic data and abstract were imported from PubMed on 01 Nov 2026.
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