Aug 2026· International Journal of Machine Learning and Cybernetics· Vol 17· 0 citations· 47 references
TL;DR
OaCMatch is proposed, a novel weak-to-strong consistency framework that explicitly leverages raw unlabeled image information into consistency learning, and consistently outperforms existing semi-supervised approaches across multiple evaluation metrics.
This work introduces a Class Semantic Distillation module, which transfers class-level semantic knowledge from labeled data to unlabeled representations through class prototype alignment in the feature space, facilitating discriminative feature learning, and develops a Low-Entropy Consistency module that dynamically em...
Dingcan Hu, Shuqi Dong, Heng-Bo Liu et al.· Computerized Medical Imaging...· 0 citations
Semi-supervised medical image segmentation methods have drawn wide attention as they reduce reliance on heavily annotated data. However, existing models suffer from confirmation bias with limited annotations, and structural or parameter coupling hinders self-correction, especially for medical images with ambiguous boun...
Dong-Sheng Wang, Xiao-Han Lang· Biomedical engineering and p...· 0 citations
Results indicate that FFT-EMatch can effectively improve pseudo-label reliability and boundary-aware representation learning under limited annotation settings, highlighting its promise for annotation-efficient medical image segmentation and its potential applicability in practical medical image analysis scenarios.
Neng-Zhao Luo, Yan-Min Luo, Yu-Tian Lin et al.· Physics in Medicine and Biol...· 0 citations
DABAL is proposed, a semi-supervised framework designed to improve both supervision reliability and contour localization and introduces a Dynamic-static Domain Adaptive Adapter (DDAA) into the Segment Anything Model (SAM) encoder to preserve stable structural priors while providing input-dependent feature compensation...
Wei-Yan Zeng, Zhi-Ming Cheng, Bin Lin et al.· Computerized Medical Imaging...· 0 citations
SAUF-Net is proposed, a Structure--Appearance Representation Learning with Uncertainty Feedback Network for semi-supervised medical image segmentation that outperforms state-of-the-art semi-supervised methods, especially under low-label settings.
Qin Lu, Zhe-Yang Jing, Yu-Jie Yang et al.· 0 citations
Multi-organ segmentation is often challenged by partially annotated datasets and domain shifts across different imaging sources. To address these limitations, we propose a two-stage learning framework that efficiently leverages partial supervision. In the first stage, the model learns from available annotations to prod...
