Semi-MedRef: Semi-Supervised Medical Referring Image Segmentation with Cross-Modal Alignment
Medical referring image segmentation (MRIS) predicts lesion masks from medical images and natural-language referring expressions, but acquiring paired pixel-level annotations and referring texts is costly. Semi-supervised learning (SSL) can alleviate this burden by exploiting unlabeled data, yet its effectiveness depends on preserving image--text alignment under strong perturbations. Existing SSL methods for referring segmentation rely on independent or simple multimodal perturbations (e.g., left--right flips), while stronger augmentations such as CutMix remain largely unexplored because they can disrupt cross-modal correspondence. We propose Semi-MedRef, a teacher--student SSL framework ...
| Authors | Yuchen Li, Ziru Wei, Zhen Zhao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 May 2026 |
| Last updated at source | 1 August 2026 |
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