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arXiv · 2608.25630

SeVeR: Selective Visual Exposure and Retrieval for 3D Medical Image Question Answering

Yaojun Hu·Danyang Tu·Yang Liu·Jiajin Zhang·Wei Fang·Zhiqiang Liu·Chunlai Dong·Yingda Xia·Haochao Ying·Jian Wu·Ling Zhang
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Abstract

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 gated attention during decoding, trained with a marginal-utility self-consistency objective that suppresses unhelpful retrieval. Experiments on BreMRIs-VQA and public benchmarks show that SeVeR improves both discriminative and generative performance while exposing substantially fewer visual tokens.

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Yaojun Hu, Danyang Tu, Yang Liu, Jiajin Zhang, Wei Fang, Zhiqiang Liu, Chunlai Dong, Yingda Xia, Haochao Ying, Jian Wu, Ling Zhang. 2026-08-26. SeVeR: Selective Visual Exposure and Retrieval for 3D Medical Image Question Answering. https://arxiv.org/abs/2608.25630

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