MetaDent: Labeling Clinical Images for Vision-Language Models in Dentistry
Vision-Language Models (VLMs) have demonstrated significant potential in medical image analysis, yet their application in intraoral photography remains largely underexplored due to the lack of fine-grained, annotated datasets and comprehensive benchmarks. To address this, we present MetaDent, a comprehensive resource that includes (1) a novel and large-scale dentistry image dataset collected from clinical, public, and web sources; (2) a semi-structured annotation framework designed to capture the hierarchical and clinically nuanced nature of dental photography; and (3) comprehensive benchmark suites for evaluating state-of-the-art VLMs on clinical image understanding. Our labeling approac...
| Authors | Meng-Xun Li, Wen-Hui Deng, Zhi-Xing Wu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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