arXiv Computer Vision October 1, 2026 By Bangwei Guo, Xiao Chen, Boris Mailhe, Jia Yao, Yiqing Wang, Ankush Mukherjee, Yikang Liu, Zheyuan Zhang, Hang Yu, Terrence Chen, Shanhui Sun

Learning Where to Look: Anatomical Grounding and Guided Attention for Cardiac MRI Vision-Language Models

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arXiv Computer Vision

Sep 10

RAU: Reference-based Anatomical Understanding with Vision Language Models

arXiv:2509.22404v2 Announce Type: replace Abstract: Anatomical understanding, which is the ability to identify, localize, or segment anatomical structures, is critical in medical image analysis; howe...

By Yiwei Li, Yikang Liu, Jiaqi Guo, Lin Zhao, Zheyuan Zhang, Xiao Chen, Boris Mailhe, Ankush Mukherjee, Terrence Chen, Shanhui Sun

llms computer-vision nlp fine-tuning multimodal

arXiv AI

Aug 5

CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement

arXiv:2608. 03890v1 Announce Type: cross Abstract: A clinically useful chest X-ray system must go beyond fluent report generation: it should classify findings with tunable decision thresholds, localize them spatially, and derive the anatomical measurements upon which many diagnoses depend.

By Mercy Prasanna Ranjit, Anirban Porya, Sathvik Joel, Niharika Vadlamudi, Nikhilesh Chowdary Eathamukkala, Prasanth V V, Abhyuday Kumara Swamy, Pranay Narhari Umredkar, Pradeep Narayan, Vivek Rajagopal, Tanuja Ganu

llms nlp multimodal benchmarks

arXiv Computer Vision

Sep 1

CheXGround: Anatomical Region Tokens for Grounded Longitudinal Chest X-ray Interpretation

arXiv:2608.30758v1 Announce Type: new Abstract: Recent radiology multi-modal language models have made substantial progress in chest X-ray report generation, visual question answering, and temporal r...

By Adonay Demewez Gebremedhin, Wessam Shehieb, Sara Alansari, Mohamad Alansari, Muzammal Naseer, Sajid Javed, Naoufel Werghi

llms nlp multimodal safety

arXiv Computation and Language

Sep 22

Representation-guided in-context learning for medical image interpretation with multimodal large language models

arXiv:2609.24057v1 Announce Type: cross Abstract: Medical image interpretation is central to diagnosis and care, yet adapting general-purpose multimodal large language models (MLLMs) often requires r...

By Minda Zhao, Fangyu Hu, Yan Luo, Yutong Yang, Jiahui Cai, Kaichen Zhou, Manling Li, Paul Liang, Yilun Du, Lucy Q. Shen, Mengyu Wang

llms nlp fine-tuning multimodal

arXiv AI

Sep 1

MR-JEPA: A General Purpose Video Foundation Model for Cardiac MRI

arXiv:2608.30975v1 Announce Type: cross Abstract: Cardiac magnetic resonance imaging (CMR) produces rich sequential data such as temporal cine videos and spatial LGE/mapping stacks, yet most deep lea...

By Athira J. Jacob, Puneet Sharma, Dorin Comaniciu, Daniel Rueckert

nlp

arXiv AI

4d ago

EyeVQA: Benchmarking Ophthalmic Vision-Language Models from Recognition to Spatial Grounding

arXiv:2609.32352v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have shown increasing potential for medical image understanding, yet their capabilities in ophthalmic imaging r...

By Gujie Shao, Zixun Xie, Xuechun Xing, Ruixiang Wang, Ziyun Lan, Yanlin Qi, Gangyi Zhang, Yuxin Yang, Dawei Li, Haiming Tang

llms computer-vision nlp multimodal benchmarks

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Related stories

RAU: Reference-based Anatomical Understanding with Vision Language Models

CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement

CheXGround: Anatomical Region Tokens for Grounded Longitudinal Chest X-ray Interpretation

Representation-guided in-context learning for medical image interpretation with multimodal large language models

MR-JEPA: A General Purpose Video Foundation Model for Cardiac MRI

EyeVQA: Benchmarking Ophthalmic Vision-Language Models from Recognition to Spatial Grounding