UCSF-PDGM-VQA: Visual Question Answering dataset for brain tumor MRI interpretation
Brain tumor diagnosis is largely dependent on Magnetic Resonance Imaging (MRI) evaluation, which requires radiologists to synthesize thousands of images across multiple 3D sequences and longitudinal studies. This process requires advanced neuro-radiology training, poses substantial cognitive load, and is highly time-consuming. Despite increasing demands in radiology, this expertise is difficult to scale, straining the current health systems. Vision-Language Models (VLMs) provide an opportunity to reduce this burden through a semi-automated, interactive interpretation of complex brain MRIs. However, they are currently underutilized in neuro-oncology due to a lack of specialized benchmarks ...
| Authors | Shiv Ghosh, Junayd Lateef, Chih-Hua Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 May 2026 |
| Last updated at source | 19 May 2026 |
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