arXiv:2607. 24743v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) hold immense potential to revolutionize clinical practice, yet deploying them in the medical domain is fundamentally a vision-centric challenge: models must absorb knowledge from heterogeneous 2D and 3D medical images, and evaluation protocols must align with radiologists' clinical practice and provide an accurate, fine-grained and factualness-driven assessment.
By Hangjie Yuan, Yichen Qian, Zhiwei Tang, Xianzhe Xu, Lirong Wu, Sicheng Yang, Jinwang Wang, Pengju Wang, Zhitao Zeng, Yizeng Han, Yan Xing, Shengxuan Luo, Tao Feng, Qing Xie, Weigen Yao, Yi Yang, Zuozhu Liu, Jiasheng Tang, Shaocheng Wang, Jitao Wang, Jiahong Dong, Weihua Chen, Feng Xu, Fan Wang
MedGEN-Bench is a new benchmark for open‑ended multimodal medical generation that addresses limitations in current medical visual benchmarks, such as query‑image misalignment, closed‑ended answer spaces, and text‑centric outputs. The dataset contains 6,422 image‑text pairs across six imaging modalities, 15 clinical tasks, and 27 subtasks, including VQA, image editing, and contextual multimodal generation pairs. Evaluation combines reference‑based fidelity metrics with a structured, checklist‑guided assessment by a medical VLM judge, and preliminary results show that image‑output tasks remain unsaturated while contextual augmentation improves image‑instruction similarity.
By Junjie Yang, Yuhao Yan, Gang Wu, Rui Qian, Zhisheng Chen, Haijiang Li, Yuhe Wu, Qichao Zhao, Dawen Tian, Xiang Wan, Fenglei Fan, Wenjian Qin, Yongquan Zhang, Feiwei Qin, Changmiao Wang
arXiv:2606. 12169v1 Announce Type: cross Abstract: High-stakes clinical use of large vision-language models (LVLMs) requires reasoning that is grounded in visual evidence and clinical knowledge, not just correct final answers.
By Negin Baghbanzadeh, Pritam Sarkar, Michael Colacci, Abeer Badawi, Adibvafa Fallahpour, Arash Afkanpour, Leonid Sigal, Ali Etemad, Elham Dolatabadi
The paper introduces MedREAL, a unified framework that aligns linguistic reasoning with spatial grounding for medical visual question answering and segmentation. MedREAL employs Seg Anchored Reasoning Pooling (SARP) to extract semantic evidence from segmentation tokens and a Reasoning-to-Visual (R2V) fusion mechanism to integrate these features into a segmentation pipeline. Using the newly created MedRAVS-13K dataset, MedREAL achieves superior performance, reporting 68.49% gIoU and 70.47% cIoU, and generates evidence masks that consistently match textual diagnoses.
By Haowen Gu, Gensheng Pei, Junzhu Mao, Qiong Wang, Mingwu Ren, Yazhou Yao
arXiv:2409.16183v2 Announce Type: replace
Abstract: Radiology is a vital and complex component of modern clinical workflow and covers many tasks. Recently, vision-language (VL) foundation models in m...
By Xiaohong Liu, Guoxing Yang, Yulin Luo, Jiaji Mao, Xiang Zhang, Haibo Wang, Zhiyang He, Ming Gao, Shanghang Zhang, Jun Shen, Guangyu Wang
arXiv:2607. 04344v1 Announce Type: cross Abstract: While Large Vision-Language Models (VLMs) demonstrate remarkable generic capabilities, their clinical reasoning in specialized domains like ocular surface diseases (OSDs) is severely hindered by a paucity of high-fidelity, multimodal instruction-tuning data.
By Hao Wei, Wenjin Qi, Dasen Dai, Minqing Zhang, Wu Yuan