Introduces the UCSF-PDGM-VQA dataset of 2387 QA pairs from 473 glioma MRI studies and demonstrates that state-of-the-art VLMs exhibit modality collapse on multi-sequence 3D medical images.
arXiv preprint arXiv:2506.18378 , year=
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BiomedAP improves robustness of biomedical VLMs to prompt variations using gated cross-modal fusion and dual-anchor constraints, outperforming baselines on 11 benchmarks.
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UCSF-PDGM-VQA: Visual Question Answering dataset for brain tumor MRI interpretation
Introduces the UCSF-PDGM-VQA dataset of 2387 QA pairs from 473 glioma MRI studies and demonstrates that state-of-the-art VLMs exhibit modality collapse on multi-sequence 3D medical images.
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BiomedAP: A Vision-Informed Dual-Anchor Framework with Gated Cross-Modal Fusion for Robust Medical Vision-Language Adaptation
BiomedAP improves robustness of biomedical VLMs to prompt variations using gated cross-modal fusion and dual-anchor constraints, outperforming baselines on 11 benchmarks.