AI RESEARCH
Towards Clinically Interpretable Ophthalmic VQA via Spatially-Grounded Lesion Evidence
arXiv CS.CV
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ArXi:2605.22414v1 Announce Type: new Visual Question Answering (VQA) holds great promise for clinical, particularly in ophthalmology, where retinal fundus photography is essential for diagnosis. However, ophthalmic VQA benchmarks primarily emphasize answer accuracy, neglecting the explicit visual evidence necessary for clinical interpretability. In this work, we