Citrus-V：通过统一医学图像定位推进临床推理的医学基础模型
Citrus-V: Advancing Medical Foundation Models with Unified Medical Image Grounding for Clinical Reasoning
AI总结：
Citrus-V是一种多模态医学基础模型，整合检测、分割和思维链推理，实现像素级病灶定位与临床诊断推理，在多个基准上超越现有开源模型。
摘要：
医学影像为临床诊断、治疗规划和手术决策提供关键证据，然而大多数现有影像模型功能狭窄，需要多个专用网络，限制了其泛化能力。尽管大规模语言和多模态模型展现出强大的推理和多任务能力，但真实世界的临床应用要求精确的视觉定位、多模态整合和思维链推理。我们提出Citrus-V，一种结合图像分析与文本推理的多模态医学基础模型。该模型整合了检测、分割和多模态思维链推理，在单一框架内实现像素级病灶定位、结构化报告生成和类似医生的诊断推理。我们提出了一种新颖的多模态训练方法，并发布了一套精选的开源数据套件，涵盖推理、检测、分割和文档理解任务。评估表明，Citrus-V在多个基准上优于现有的开源医学模型和专家级影像系统，提供从视觉定位到临床推理的统一流程，支持精确的病灶量化、自动化报告和可靠的第二意见。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
