MedSIGHT: Towards Grounded Visual Comprehension in Medical Large Vision-Language Models
Medical large vision-language models (Med-LVLMs) have recently achieved remarkable progress in vision-language comprehension and medical image segmentation. However, existing models still struggle to unify these two capabilities, which is essential for achieving clinically reasoning that connects visual findings with semantic interpretation. We present MedSIGHT, a unified framework that equips Med-LVLMs with structured, pixel-level understanding for grounded visual comprehension. MedSIGHT introduces a novel Region Perceiver module that produces region-centric tokens, encoding spatial information directly into representation space of the language model. We further propose a medical region ...
| Authors | Aofei Chang, Le Huang, Alex James Boyd et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 June 2026 |
| Last updated at source | 4 June 2026 |
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