Class Sensitive Calibration and Discrepancy-Aware Synthesis for Semi-Supervised Medical Image Segmentation

Accurate organ segmentation is essential for medical image analysis. Semi-supervised medical image segmentation reduces annotation costs while maintaining high segmentation precision. However, it still suffers from distribution shift between labeled and unlabeled data and severe class imbalance, which degrades pseudo-label quality and severely hinders segmentation of critical targets. To address these issues, we propose a novel semi-supervised framework integrating two key strategies. Firstly, we propose a Class-Sensitive Temperature Scaling (CSTS) strategy that dynamically calibrates logit adjustment by modeling class-wise consistency and confusion, applying both global and local regulation, and leveraging a dual-head decoupled architecture for robust per-class adaptation to distribution shift. Secondly, we introduce a Discrepancy-Aware Sampling Strategy (DSS) that forms a closed-loop feedback system to guide conditional diffusion models in generating high-quality samples with enhanced representations for minority classes, boosting segmentation performance. Experiments on two public abdominal multi-organ segmentation datasets demonstrate that our method outperforms state-of-the-art techniques, achieving comprehensive improvements in segmentation accuracy with particularly significant gains for minority classes. The code of our method is available at https://github.com/LiuTingWed/C2DS LiuTingWed/C2DS.