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Noise-Aware Visual Representation Learning for Medical Visual Question Answering

I Putu Adi Pratama, Bahadorreza Ofoghi, Atul Sajjanhar, Shang Gao
Jun 5, 2026 at 04:00
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arXiv:2606.05535v1 Announce Type: cross Abstract: Medical visual question answering (Med-VQA) has strong potential for clinical decision support by enabling AI models to interpret medical images and answer clinically relevant queries. Recent approaches typically connect off-the-shelf vision encoders with large language models (LLMs) through...

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