@inproceedings{hu-etal-2026-sever,
title = "{S}e{V}e{R}: Selective Visual Exposure and Retrieval for 3{D} Medical Image Question Answering",
author = "Hu, Yaojun and
Tu, Danyang and
Liu, Yang and
Zhang, Jiajin and
Fang, Wei and
Liu, Zhiqiang and
Dong, Chunlai and
Xia, Yingda and
Ying, Haochao and
Wu, Jian and
Zhang, Ling",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1666/",
pages = "37874--37903",
ISBN = "979-8-89176-459-0",
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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<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.</abstract>
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%0 Conference Proceedings
%T SeVeR: Selective Visual Exposure and Retrieval for 3D Medical Image Question Answering
%A Hu, Yaojun
%A Tu, Danyang
%A Liu, Yang
%A Zhang, Jiajin
%A Fang, Wei
%A Liu, Zhiqiang
%A Dong, Chunlai
%A Xia, Yingda
%A Ying, Haochao
%A Wu, Jian
%A Zhang, Ling
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F hu-etal-2026-sever
%X 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.
%U https://aclanthology.org/2026.emnlp-main.1666/
%P 37874-37903
Markdown (Informal)
[SeVeR: Selective Visual Exposure and Retrieval for 3D Medical Image Question Answering](https://aclanthology.org/2026.emnlp-main.1666/) (Hu et al., EMNLP 2026)
ACL
- Yaojun Hu, Danyang Tu, Yang Liu, Jiajin Zhang, Wei Fang, Zhiqiang Liu, Chunlai Dong, Yingda Xia, Haochao Ying, Jian Wu, and Ling Zhang. 2026. SeVeR: Selective Visual Exposure and Retrieval for 3D Medical Image Question Answering. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 37874–37903, Budapest, Hungary. Association for Computational Linguistics.