Localization-Infused Vision-Language Semantic Fusion for Text-Guided Medical Image Segmentation
Medical image segmentation is essential for modern computer-aided medicine. Recently, text-guided segmentation has shown promise by incorporating clinician-formulated textual reports as semantic guidance for image segmentation. These textual reports contain language descriptions about the appearance, location, and neighboring anatomy of segmentation targets, providing explicit guidance for target localization and delineation. Existing text-guided segmentation methods typically extract textual semantics implicitly through a pretrained text encoder and then integrate vision-language semantics via straightforward image-text feature fusion. However, these methods do not explicitly capture tar...
| Authors | Songyue Han, Mingye Zou, Shuchang Ye et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 July 2026 |
| Last updated at source | 22 August 2026 |
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