SeVeR: Selective Visual Exposure and Retrieval for 3D Medical Image Question Answering
Volumetric medical VQA requires reasoning over long and redundant 3D visual token sequences, especially in multi-sequence MRI where complementary modalities provide diverse diagnostic cues but expose the decoder to many repeated anatomical regions. To investigate reasoning under multi-sequence visual redundancy, we first introduce BreMRIs-VQA, a clinically curated breast MRI benchmark with 1.19M QA pairs from 71.0K sequences and 12.9K patients, covering both free-text and multiple-choice questions. We further propose SeVeR, a selective visual exposure framework that compresses dense volumes into modality-wise prototypes and retrieves complementary multi-level evidence with change-aware ga...
| Authors | Yaojun Hu, Danyang Tu, Yang Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 August 2026 |
| Last updated at source | 26 August 2026 |
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