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arXiv 2603.03437cs.CV
超越准确率：评估多模态医学推理中的视觉语义
Beyond Accuracy: Evaluating Visual Grounding In Multimodal Medical Reasoning
The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）；Cohere Labs（Cohere实验室）；Eisai Inc.（艾伯维公司）；Indian Institute of Science, Bangalore（班加罗尔印度科学研究院）
Recent work shows that text-only reinforcement learning with verifiable rewards (RLVR) can match or outperform image-text RLVR on multimodal medical VQA benchmarks, suggesting current evaluation protocols may fail to measure causal visual dependence. We introduce a counterfactual evaluation framework using real, blank, and shuffled images across four medical VQA benchmarks: PathVQA, PMC-VQA, SLAKE, and VQA-RAD. Beyond accuracy, we measure Visual Reliance Score (VRS), Image Sensitivity (IS), and introduce Hallucinated Visual Reasoning Rate (HVRR) to detect cases where models generate visual claims despite producing image-invariant answers. Our findings reveal that RLVR improves accuracy while degrading visual grounding: text-only RLVR achieves negative VRS on PathVQA (-0.09), performing better with mismatched images, while image-text RLVR reduces image sensitivity to 39.8% overall despite improving accuracy. On VQA-RAD, both variants achieve 63% accuracy through different mechanisms: text-only RLVR retains 81% performance with blank images, while image-text RLVR shows only 29% image sensitivity. Models generate visual claims in 68-74% of responses, yet 38-43% are ungrounded (HVRR). These findings demonstrate that accuracy-only rewards enable shortcut exploitation, and progress requires grounding-aware evaluation protocols and training objectives that explicitly enforce visual dependence.
MedSG-Bench: A Benchmark for Medical Image Sequences Grounding
Beijing University of Posts and Telecommunications（北京邮电大学）；Tianjin University（天津大学）；South China Hospital, Medical School, Shenzhen University（深圳大学医学院南方医院）
Do Medical Vision Language Models Actually See? A Counterfactual Grounding Framework and Hard-Negative Contrastive Training for Visually-Reliant Medical VLMs
The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）；Eisai Inc.（卫材株式会社）；IISc, Bangalore（印度科学研究所班加罗尔分校）；Cohere Labs Community（Cohere实验室社区）
Tianjin University（天津大学）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；Singapore General Hospital（新加坡中央医院）；Saw Swee Hock School of Public Health, National University of Singapore National University Health System（新加坡国立大学国立大学卫生系统苏瑞福公共卫生学院）
Medical phrase grounding (MPG) aims to locate the most relevant region in a medical image, given a phrase query describing certain medical findings, which is an important task for medical image analysis and radiological diagnosis. However, existing visual grounding methods rely on general visual features for identifying objects in natural images and are not capable of capturing the subtle and specialized features of medical findings, leading to sub-optimal performance in MPG. In this paper, we propose MedRPG, an end-to-end approach for MPG. MedRPG is built on a lightweight vision-language tran…
MIMO: A medical vision language model with visual referring multimodal input and pixel grounding multimodal output
School of Software & Microelectronics, Peking University（软件与微电子学院，北京大学）；School of Computer Science, Peking University（计算机学院，北京大学）；National Engineering Research Center for Software Engineering, Peking University（软件工程国家工程研究中心，北京大学）；Peking University Sixth Hospital（北京大学第六医院）；Augusta University（奥古斯塔大学）；Peking University First Hospital（北京大学第一医院）
Uncertainty-aware Medical Diagnostic Phrase Identification and Grounding
College of Computer Science, Sichuan University（四川大学计算机学院）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算机系）；Department of Radiology, West China Hospital, Sichuan University（四川大学华西医院放射科）；College of Intelligence and Computing, Tianjin University（天津大学智能与计算学院）；Department of Mathematics, Sichuan University（四川大学数学系）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-Sen University（中山大学深圳校区网络科学与技术学院）；Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and the Singapore Eye Research Institute, Singapore National Eye Centre（新加坡国立大学 Yong Loo Lin 医学院眼科系及新加坡眼科研所、新加坡国家眼科中心）
Visual question answering (VQA) is the multi-modal task of answering natural language questions about an input image. Through cross-dataset adaptation methods, it is possible to transfer knowledge from a source dataset with larger train samples to a target dataset where training set is limited. Suppose a VQA model trained on one dataset train set fails in adapting to another, it is hard to identify the underlying cause of domain mismatch as there could exists a multitude of reasons such as image distribution mismatch and question distribution mismatch. At UCLA, we are working on a VQG module t…
Enhancing Medical Visual Grounding via Knowledge-guided Spatial Prompts
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Yong Yoo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院）；Institute of High-Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）
CoTBox-TTT: Grounding Medical VQA with Visual Chain-of-Thought Boxes During Test-time Training
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港大学深圳研究院数据科学学院）
MedGround: Bridging the Evidence Gap in Medical Vision-Language Models with Verified Grounding Data
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；DAMO Academy, Alibaba Group（阿里巴巴集团 DAMO 院）；Hupan Lab, Zhejiang Province（浙江省 Hupan 实验室）
Knowledge to Sight: Reasoning over Visual Attributes via Knowledge Decomposition for Abnormality Grounding
Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Imperial College London（伦敦帝国学院）；University of Trento（特伦托大学）；Helmholtz AI and Helmholtz Munich（海德堡人工智能与海德堡慕尼黑）；King’s College London（伦敦国王学院）
MedSPOT: A Workflow-Aware Sequential Grounding Benchmark for Clinical GUI
Gaash Research Lab, National Institute of Technology Srinagar, India（加什研究实验室，印度锡里纳杰尔国家理工学院）；e\& Group, UAE（e&集团，阿联酋）；Mohammad Bin Zayed University of Artificial Intelligence (MBZUAI), UAE（穆罕默德·本·扎耶德人工智能大学（MBZUAI），阿联酋）；King Abdullah University of Science and Technology (KAUST), Saudi Arabia（国王 Abdullah 科学技术大学（KAUST），沙特阿拉伯）
Enhancing Abnormality Grounding for Vision Language Models with Knowledge Descriptions
Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Helmholtz AI（亥姆霍兹人工智能研究所）；Helmholtz Munich（亥姆霍兹慕尼黑中心）；Imperial College London（伦敦帝国学院）；King’s College London（伦敦国王学院）
Synergistic Perception-Reasoning Governance: Grounding Medical MLLMs with Verifiable Anatomical Evidence
Huazhong University of Science and Technology（华中科技大学）；Imperial Global Singapore, Imperial College London（帝国理工学院新加坡全球中心）；Nanyang Technological University（南洋理工大学）；National University of Singapore（新加坡国立大学）；Anhui University（安徽大学）
MedFM-Robust: Benchmarking Robustness of Medical Foundation Models
Beijing Normal University, China（北京师范大学）；University of Exeter, United Kingdom（埃克塞特大学）；University College London, United Kingdom（伦敦大学学院）；Mohamed bin Zayed University of Artificial Intelligence, United Arab Emirates（穆罕默德·本·扎耶德人工智能大学）；University of Surrey, United Kingdom（萨里大学）
Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding
Australian Institute for Machine Learning, University of Adelaide（澳大利亚机器学习研究所，阿德莱德大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Macquarie University（麦考瑞大学）；Hanoi University of Science and Technology（河内科学技术大学）；University of Wollongong（沃林根大学）；Flinders University（弗林德斯大学）
Detecting and Evaluating Medical Hallucinations in Large Vision Language Models
Academy for Engineering and Technology, Fudan University（复旦大学工程与技术学院）；Tencent Youtu Lab（腾讯优图实验室）；Cognition and Intelligent Technology Laboratory（认知与智能技术实验室）
Detecting Clinical Hallucinations in LVLMs via Counterfactual Visual Grounding Uncertainty
School of Intelligent Science and Technology, Nanjing University（南京大学智能科学与技术学院）；National Institute of Healthcare Data Science at Nanjing University（南京大学医疗数据科学国家研究院）；School of Biomedical Engineering, Nanjing University（南京大学生物医学工程学院）
Better Eyes, Better Thoughts: Why Vision Chain-of-Thought Fails in Medicine
Department of Computer Science, City University of Hong Kong (Dongguan)（香港城市大学（东莞）计算机科学系）；College of Computing and Data Science (CCDS), Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机科学学院）
Med-StepBench: A Hierarchical Reasoning Framework for Evaluating Hallucinations in Medical Vision-Language Models
AI4LIFE, Hanoi University of Science and Technology, Vietnam（AI4LIFE，越南科学与技术大学）；SAMOVAR, Télécom SudParis, Institut Polytechnique de Paris, France（SAMOVAR，法国电信南巴黎学院，巴黎理工学院）；Military Central Hospital, Vietnam（越南108军中心医院）
Automated Report-Derived Oncology VQA Benchmark for Evaluating Vision-Language Models on 3D Medical Imaging
UCSF–UC Berkeley Joint Graduate Program in Bioengineering（UCSF-伯克利生物工程联合研究生项目）；Department of Radiology, UCSF（UCSF放射科）；Department of Radiation Oncology, UCSF（UCSF放射肿瘤科）
MedReaMM: Evaluating Large Multimodal Models on Expert-Level Clinical Diagnostic Synthesis
The First Affiliated Hospital with Nanjing Medical University（南京医科大学第一附属医院）；Jiangsu Province Hospital（江苏省医院）；Huazhong University of Science Technology（华中科技大学）；Fudan University（复旦大学）
MedVL-SAM2: A unified 3D medical vision-language model for multimodal reasoning and prompt-driven segmentation
Department of Biomedical Engineering, University of Florida（佛罗里达大学生物医学工程系）；Department of Radiology, University of Florida（佛罗里达大学放射学系）；Research Computing, University of Florida（佛罗里达大学研究计算中心）；Department of Medicine, University of Florida（佛罗里达大学医学系）；Department of Radiology, UC San Francisco（旧金山大学放射学系）
