NeuroQA: A Large-Scale Image-Grounded Benchmark for 3D Brain MRI Understanding
NeuroQA is a newly introduced benchmark aimed at enhancing visual question answering in 3D brain MRI analysis. It includes a comprehensive dataset of 56,953 question-answer pairs derived from 12,977 subjects across various clinical domains. The benchmark emphasizes the importance of image-grounding in medical diagnostics, offering a robust evaluation framework for AI models.
- ▪NeuroQA features 56,953 QA pairs from 12,977 subjects across 12 datasets, covering ages from 5 to 104.
- ▪The benchmark evaluates 11 clinically grounded reasoning skills using Yes/No, multiple-choice, and open-ended formats.
- ▪A two-tier release strategy includes public QA pairs for open-access datasets and scripts for restricted datasets.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20525 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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Computer Science > Computer Vision and Pattern Recognition arXiv:2605.20525 (cs) [Submitted on 19 May 2026] Title:NeuroQA: A Large-Scale Image-Grounded Benchmark for 3D Brain MRI Understanding Authors:Mohammad H. Abbasi, Favour Nerrise, Shaurnav Ghosh, Ridvan Yesiloglu, Yuncong Mao, Bailey Trang, Mohammad Asadi, Merryn Daniel, Gustavo Chau Loo Kung, Ken Chang, Pavan Pinkesh Shah, Adam Turnbull, Kyan Younes, Seena Dehkharghani, Ehsan Adeli (Stanford University) View a PDF of the paper titled NeuroQA: A Large-Scale Image-Grounded Benchmark for 3D Brain MRI Understanding, by Mohammad H.
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