SageProto: Mitigating Class-Wise Learning Disparity in Semi-Supervised Multi-Organ Segmentation

Medical image segmentation requires dense expert annotations, motivating semi-supervised learning with limited labeled and abundant unlabeled data. However, anatomical classes differ markedly in learning difficulty, creating pronounced class-wise learning disparity under limited supervision: well-learned classes form reliable visual representations, whereas difficult classes remain under-optimized. In semi-supervised learning, unlabeled supervision and class representation learning remain closely tied to model predictions and visual evidence. These class-wise disparities may therefore persist throughout training. We propose SageProto, a semantic-anchor prototype framework for semi-supervised multi-organ segmentation that introduces textual semantic references into representation learning, prototype estimation, and decoding. SageProto encodes anatomical descriptions into textual semantic anchors that provide class-specific references independent of visual representation strength. These anchors modulate visual representations and initialize class prototypes, which are iteratively rectified with visual evidence and used as class-specific queries for textual and visual retrieval during decoding. Prototype regularization further constrains cross-view consistency, class semantics, and inter-class geometry. By reducing difficult-class dependence on weak visual evidence, SageProto mitigates class-wise learning disparity. Extensive experiments on two multi-organ segmentation benchmarks involving 11 semi-supervised methods show that SageProto improves the best existing Dice by 2.08% on Synapse and 2.31% on FLARE22.