Negative Semantics Drive Expert Specialization in Semi-Supervised Medical Image Segmentation

Recent semi-supervised medical image segmentation methods have increasingly explored clinical text to alleviate the scarcity of pixel-level annotations. However, existing methods primarily use positive semantics to describe desired predictions, while overlooking information about which visual patterns should be rejected. This paper shows that structured negative semantics can provide explicit signals for learning complementary visual experts. Building on this insight, RCEA-MoE structures negative descriptions into disease, location, and extent factors to spe- cialize heterogeneous visual experts. Positive–negative semantic contrasts induce factor-specific spatial corrections, which are adaptively aggregated by an image– positive-text router, while an exponential moving average (EMA) teacher pro- vides confidence-aware supervision for unlabeled images. In a controlled ablation on QaTa-COV19 with 25% labeled training data, RCEA-MoE achieves 90.84% Dice and 83.22% mIoU, improving over the single-expert visual baseline by 3.87 and 6.27 percentage points, respectively. These findings highlight the potential of combining structured negative guidance, heterogeneous experts, and reliability- aware learning for label-efficient medical image segmentation. Code is available at: https://anonymous.4open.science/r/RCEA-MoE-91D1.