Decoupling Target Semantics via Text-Anchored Visual Contrast for Semi-Supervised Medical Image Segmentation

Semi-supervised learning (SSL) provides an effective means of reducing reliance on large-scale annotated datasets by leveraging unlabeled data. However, existing SSL methods often struggle with semantic ambiguity, especially under limited supervision. Recent studies have incorporated textual information to provide contextual guidance, yet most focus on feature fusion rather than emphasizing target semantics critical for segmentation. In this paper, we proposed a novel Text-anchored Visual Decoup