Medical VQA

VQA: Visual Question Answering

Follow this topic Ask about this topic

Momentum

10 papers in the last four weeks, up 67% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 115

All topics

UniReason-Med: A Shared Grounded Reasoning Interface for 2D-to-3D Transfer in Medical VQA
Jun 10, 2026Mengzhuo Chen, Yan Shu, Chi Liu +4Medical VQA VLM Reasoning
IQuest Research
A Controlled Audit of Pretraining Contamination in Public Medical Vision-Language Benchmarks
Jun 8, 2026Bruce Changlong Xu, Lan Wu, Alexander RyuVLM Evaluation Medical VQA
Stanford University Stanford, CA, USA · University of California, Berkeley Berkeley, CA, USA · Mayo Clinic Rochester, MN, USA
Noise-Aware Visual Representation Learning for Medical Visual Question Answering
Jun 4, 2026I Putu Adi Pratama, Bahadorreza Ofoghi, Atul Sajjanhar +1VLM Robustness Denoising Autoencoders
Deakin University, 221 Burwood Highway, Burwood, Victoria 3125, Australia
Attention Consistent Longitudinal Medical Visual Question Answering Guided by Vision Foundation Models
Jun 3, 2026Jialin Wu, Qianru Zhang, Georges El Fakhri +1Vision Foundation Model Adaptation Medical VQA
Yale Biomedical Imaging Institute New Haven, CT 06510
Does Language Shift Break Medical Vision-Language Models? Indonesian Radiology Visual Question Answering Case Study
Jun 2, 2026Pieter Christy Yan Yudhistira, Dzaki Rafif Malik, Novanto YudistiraVLM Evaluation VLM Robustness
Intelligent System Laboratory, Faculty of Computer Science Brawijaya University, Malang, Indonesia
Automated Report-Derived Oncology VQA Benchmark for Evaluating Vision-Language Models on 3D Medical Imaging
Jun 1, 2026Bo Liu, Hanxue Gu, Xiangru Li +6VLM Evaluation Medical Imaging
UCSF–UC Berkeley Joint Graduate Program in Bioengineering · Department of Radiology, UCSF · Department of Radiation Oncology, UCSF
Ask4VG: Risk-Aware Question Selection for Reducing Prior-Driven Answers in Medical VQA
May 31, 2026Xiaorong Zhu, Qiang Li, Zibo Xu +2Medical VQA VLM Hallucination Mitigation
School of Microelectronics, Tianjin University, Tianjin 300072, China · DISI, University of Trento, Trento, Italy · School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Ryze: Evidence-Enriched Data Synthesis from Biomedical Papers
May 30, 2026Yeqi Huang, Yue Chen, Yanwei Ye +2VLM Adaptation Synthetic Data Generation
University of Edinburgh, United Kingdom
Pocket-Dentist: On-Device Dental Image Understanding via Efficient Multimodal Large Language Models
May 28, 2026Kai Bian, Xucheng Guo, Bin Chen +4Medical Imaging Efficient VLM Inference
The University of Auckland, New Zealand · Shandong University, China · The University of Melbourne, Australia
VITAL: Visual-Semantic Dual Supervision for Enhanced and Interpretable Latent Reasoning in Medical MLLMs
May 27, 2026Qiaoru Li, Shaotian Liang, Jintao Chen +4Medical VQA VLM Interpretability
3Tencent · 1Zhejiang University · 2Shanghai AI Laboratory +1
OphIn-500K: Curating Web-Scale Visual Instructions for Scaling Ophthalmic Multimodal Large Language Models
May 27, 2026Xuanzhao Dong, Wenhui Zhu, Xiwen Chen +13Medical Image Analysis Medical VQA
1Arizona State University · 2Clemson University · 3Washington University in St. Louis +5
Towards Reliable Fetal Ultrasound Interpretation with Multi-Agent Collaboration
May 25, 2026Xiaotian Hu, Mingxuan Liu, Junwei Huang +13Multi-Agent LLM Systems Medical VQA
Tsinghua University, Beijing, China · University of California San Diego, La Jolla, CA, USA · West China Second University Hospital, Sichuan University, Chengdu, China
Parameter-Efficient VLMs for Gastrointestinal Endoscopy: Medical Image Generation and Clinical Visual Question Answering
May 24, 2026Ojonugwa Oluwafemi Ejiga Peter, Frederick Akor Ejiga, Fahmi Khalifa +1Endoscopy Medical Imaging
Computer Science Department, Morgan State University, Baltimore, USA. · International Organization for Migration (IOM), Geneva, Switzerland. · Electrical & Computer Engineering Department, Morgan State University, Baltimore, USA.
EchoVQA: Enabling Conversational Assistance for Point-of-Care Cardiac Ultrasound
May 22, 2026Filippos Bellos, Yutong Li, Jessie N Dong +6Medical VQA Echocardiography
University of Michigan · University of Pennsylvania · Independent Scientist
PathNavigate: A Training-Free Pathology Agent with Surprise-Guided Scan and Shared Slide Memory for Whole-Slide Image VQA
May 22, 2026Chunze Yang, Qidong Liu, Wenjie Zhao +10Healthcare Medical VQA
School of Comp. Science & Technology, Xi’an Jiaotong University · Tencent Jarvis Lab · University of Cambridge
RoboSurg-VQA: A Multimodal Benchmark for Surgical Segmentation-Aware Visual Question Answering
May 21, 2026Chengyi Zhang, Zi Ye, Ziyang WangMedical VQA Medical Image Benchmarks
Swansea University, UK · Maynooth University, Ireland · Aston University, UK
Towards Clinically Interpretable Ophthalmic VQA via Spatially-Grounded Lesion Evidence
May 21, 2026Xingyue Wang, Bo Liu, Meng Wang +4Medical VQA Fundus Imaging
Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China · The Hong Kong Polytechnic University, Hong Kong S.A.R., China. · National University of Singapore, Singapore. +2
JMed48k: A Multi-Profession Japanese Medical Licensing Benchmark for Vision-Language Model Evaluation
May 21, 2026Yue Xun, Junyu Liu, Qian Niu +10VLM Evaluation Healthcare
The Hong Kong Polytechnic University · Kyoto University · The University of Tokyo +3
Look-Closer-Then-Diagnose: Confidence-Aware Ultrasound VQA via Active Zooming
May 20, 2026Yue Zhou, Erxuan Wu, Yikang Sun +5Visual Question Answering Uncertainty Estimation for VLA Models
Computer Aided Medical Procedures (CAMP), TU Munich, Germany · Munich Center for Machine Learning (MCML), Munich, Germany · Zhongshan Hospital, Fudan University, China +1
VIHD: Visual Intervention-based Hallucination Detection for Medical Visual Question Answering
May 20, 2026Jiayi Chen, Benteng Ma, Zehui Liao +3Hallucination Detection in VLMs Healthcare
Department of Data Science & AI, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia · Hong Kong Polytechnic University, Hong Kong SAR, China · Alfred Health Radiology, Alfred Health, Melbourne, VIC 3004, Australia +1
NeuroQA: A Large-Scale Image-Grounded Benchmark for 3D Brain MRI Understanding
May 19, 2026Mohammad H. Abbasi, Favour Nerrise, Shaurnav Ghosh +12Healthcare 3D Medical Imaging
Stanford University · 1Stanford University, Stanford, CA, USA
Rethinking Visual Attribution for Chest X-ray Reasoning in Large Vision Language Models
May 19, 2026Guangzhi Xiong, Qiao Jin, Sanchit Sinha +2Medical VQA VLM Interpretability
University of Virginia · National Institutes of Health
$M^3 QuestionIng$ : Multi-modal Multi-span Medical Question Answering
May 19, 2026Anisha Saha, Vaibhav Rathore, Abhisek Tiwari +3Medical VQA Multimodal QA
Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrucken, Germany · Indian Institute of Technology, Bombay, Mumbai, India · Clinical AI Assistance, Gurugram, India +2
Wasserstein Equilibrium Decoding for Reliable Medical Visual Question Answering
May 18, 2026Luca Hagen, Johanna P. Müller, Weitong Zhang +2Healthcare Efficient VLM Inference
Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany · Imperial College London, London, United Kingdom · University College London, London, United Kingdom
How Good LLMs Are at Answering Bangla Medical Visual Questions? Dataset and Benchmarking
May 18, 2026Rafid Ahmed, Intesar Tahmid, Mir Sazzat Hossain +3VLM Evaluation Multilingual Language Model Evaluation
Penta Global Limited, Bangladesh · Center for Computational & Data Sciences, Independent University, Bangladesh
SurgLQA: Scalable Long-Horizon Surgical Video Question Answering
May 18, 2026Diandian Guo, Xikai Yang, Ruiyang Li +2Temporal Video Grounding Test-Time Scaling
The Chinese University of Hong Kong
UCSF-PDGM-VQA: Visual Question Answering dataset for brain tumor MRI interpretation
May 16, 2026Shiv Ghosh, Junayd Lateef, Chih-Hua Liu +3VLM Evaluation Medical VQA
Fung Institute for Engineering Leadership University of California, Berkeley Berkeley, CA 94709 · Department of Radiology University of California, San Francisco San Francisco, CA 94158 · Department of Radiology University of California, San Francisco San Francisco, CA 94143 +1
MI-CXR: A Benchmark for Longitudinal Reasoning over Multi-Interval Chest X-rays
May 15, 2026Sunghwan Steve Cho, Yunseok Han, Jaeyoung DoVLM Evaluation Healthcare
AIDAS Laboratory, 1ECE & 2IPAI, Seoul National University
RoiMAM: Region-of-Interest Medical Attention Model for Efficient Vision-Language Understanding
May 15, 2026Jiayan Yang, Zhuoyu Wu, Wenqi FangHealthcare Efficient VLM Inference
Shenzhen Institutes of Advanced Technology, Chinese Academy of Science · CyPhi(ΨΦ) AI Research Lab, School of IT, Monash University, Malaysia Campus
Verification Mirage: Mapping the Reliability Boundary of Self-Verification in Medical VQA
May 11, 2026Ruinan Jin, Beidi Zhao, Myeongkyun Kang +2VLM Evaluation Vision-Language Models
1The University of British Columbia · 2Vector Institute · 3Redmin University of China

Jun 10, 2026, cs.CVJ/K move · Enter open · S save

UniReason-Med: A Shared Grounded Reasoning Interface for 2D-to-3D Transfer in Medical VQA

Mengzhuo Chen, Yan Shu, Chi Liu, Hongming Piao+3

IQuest Research

Medical VQA VLM Reasoning Transfer Learning Visually Grounded Reasoning Medical Image Grounding 3D VQA

Open paper AskPDF arXiv

We study whether grounded reasoning supervision from abundant 2D medical images can improve 3D medical VQA when both input types are aligned through a common reasoning interface. We introduce UniReason-Med, a single-checkpoint framework that processes either a 2D image or a slice-serialized 3D volume at inference time, generating interleaved textual reasoning and localized visual evidence through shared box syntax, region-token injection, and a common grounded reasoning policy. To train this interface, we construct UniMed-CoT, a 220K instruction-tuning dataset with interleaved textual reasoning and grounded visual evidence, including 170K 2D and 50K 3D samples. Through supervised fine-tuning followed by outcome-level reinforcement learning, UniReason-Med learns to generate grounded reasoning traces without IoU/Dice-based localization rewards during RL. Data-mixture and component ablations show that joint 2D+3D grounded supervision substantially improves 3D reasoning over 3D-only training, while grounding and region-token injection consistently benefit both 2D and 3D tasks. These results suggest that a shared grounded reasoning interface can transfer reasoning structure from 2D images to slice-serialized volumetric medical understanding. The code and data are publicly available at https://github.com/IQuestLab/unireason-med.

Momentum

Related topics

Latest papers 115