Ronald Fecso, José Morano, Botond Fazekas, Paul Widmann-Sedlnitzky, Dorota Himel, Laetita Hinterhuber, Virginia Mares, Andreas Pollreisz · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31205 · open-access PDF
멀티모델 대형 언어 모델 (MLLMs)은 의료 이미지에 관한 임상 질문에 답할 수 있지만 신뢰할 수있는 사용은 시각적 토양을 필요로합니다 : 모델은 그들이보고하는 병리학을 지역화해야합니다. MLLM이 망막 광적 일관성 탐지 (OCT)에서 임상 발견을 시각적으로 토양 할 수 있는지 여부는 대부분 탐지되지 않습니다.이 격차를 해결하기 위해 우리는 망막 OCT에서 생체 마커 시각적 토양을위한 첫 번째 벤치마크 인 RetinaGroundBench를 소개합니다.
Machine translation (Meta M2M100); the English original follows.
Multimodal large language models (MLLMs) can answer clinical questions about medical images, but trustworthy use requires visual grounding: models should localize the pathology they report rather than rely on spurious shortcuts. Whether MLLMs can visually ground clinical findings in retinal optical coherence tomography (OCT), the most widely used imaging modality in ophthalmology, remains largely unexplored. To address this gap, we introduce RetinaGroundBench, the first benchmark for biomarker visual grounding in retinal OCT. It comprises 815 B-scans exhaustively annotated by ophthalmologists
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