RadGrounder

RefRad2D dataset and RadGrounder model enable spatially grounded radiology VLMs without manual annotations

Researchers introduce RefRad2D, a 1.2M-pair bilingual (German/English) CT and MR image-text dataset generated automatically via LLM curation and automated segmentation, requiring no manual spatial annotations. The accompanying RadGrounder model jointly performs report generation, VQA, and spatial grounding via bounding-box or segmentation outputs. On external benchmarks Slake and VQA-RAD, RadGrounder matches specialized medical VLMs while adding grounding supervision without degrading language quality. The work demonstrates that large-scale automatically curated clinical data can transfer to downstream medical VQA tasks.