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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.19090 |
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| _version_ | 1866909803826118656 |
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| author | Wang, Guoxin Zhao, Jun Liu, Xinyi Liu, Yanbo Cao, Xuyang Li, Chao Liu, Zhuoyun Sun, Qintian Zhou, Fangru Xing, Haoqiang Yang, Zhenhong |
| author_facet | Wang, Guoxin Zhao, Jun Liu, Xinyi Liu, Yanbo Cao, Xuyang Li, Chao Liu, Zhuoyun Sun, Qintian Zhou, Fangru Xing, Haoqiang Yang, Zhenhong |
| contents | Medical imaging provides critical evidence for clinical diagnosis, treatment planning, and surgical decisions, yet most existing imaging models are narrowly focused and require multiple specialized networks, limiting their generalization. Although large-scale language and multimodal models exhibit strong reasoning and multi-task capabilities, real-world clinical applications demand precise visual grounding, multimodal integration, and chain-of-thought reasoning. We introduce Citrus-V, a multimodal medical foundation model that combines image analysis with textual reasoning. The model integrates detection, segmentation, and multimodal chain-of-thought reasoning, enabling pixel-level lesion localization, structured report generation, and physician-like diagnostic inference in a single framework. We propose a novel multimodal training approach and release a curated open-source data suite covering reasoning, detection, segmentation, and document understanding tasks. Evaluations demonstrate that Citrus-V outperforms existing open-source medical models and expert-level imaging systems across multiple benchmarks, delivering a unified pipeline from visual grounding to clinical reasoning and supporting precise lesion quantification, automated reporting, and reliable second opinions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19090 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Citrus-V: Advancing Medical Foundation Models with Unified Medical Image Grounding for Clinical Reasoning Wang, Guoxin Zhao, Jun Liu, Xinyi Liu, Yanbo Cao, Xuyang Li, Chao Liu, Zhuoyun Sun, Qintian Zhou, Fangru Xing, Haoqiang Yang, Zhenhong Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Medical imaging provides critical evidence for clinical diagnosis, treatment planning, and surgical decisions, yet most existing imaging models are narrowly focused and require multiple specialized networks, limiting their generalization. Although large-scale language and multimodal models exhibit strong reasoning and multi-task capabilities, real-world clinical applications demand precise visual grounding, multimodal integration, and chain-of-thought reasoning. We introduce Citrus-V, a multimodal medical foundation model that combines image analysis with textual reasoning. The model integrates detection, segmentation, and multimodal chain-of-thought reasoning, enabling pixel-level lesion localization, structured report generation, and physician-like diagnostic inference in a single framework. We propose a novel multimodal training approach and release a curated open-source data suite covering reasoning, detection, segmentation, and document understanding tasks. Evaluations demonstrate that Citrus-V outperforms existing open-source medical models and expert-level imaging systems across multiple benchmarks, delivering a unified pipeline from visual grounding to clinical reasoning and supporting precise lesion quantification, automated reporting, and reliable second opinions. |
| title | Citrus-V: Advancing Medical Foundation Models with Unified Medical Image Grounding for Clinical Reasoning |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2509.19090 |