pith:WN5NGGKG
PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering
A generative model trained on a 227k-pair medical VQA dataset from literature outperforms prior systems on clinical benchmarks after fine-tuning.
arxiv:2305.10415 v6 · 2023-05-17 · cs.CV
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We train the proposed model on PMC-VQA and then fine-tune it on multiple public benchmarks, e.g., VQA-RAD, SLAKE, and Image-Clef-2019, significantly outperforming existing MedVQA models in generating relevant, accurate free-form answers.
The PMC-VQA dataset constructed from literature sources provides representative coverage of real clinical images and questions without systematic biases from publication practices or selection effects.
PMC-VQA dataset and MedVInT model achieve better generative performance on medical VQA benchmarks by visual instruction tuning on a newly constructed large-scale dataset.
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| First computed | 2026-05-17T23:38:49.836419Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
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| Schema | pith-number/v1.0 |
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Canonical record JSON
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