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arXiv 2510.10011cs.CV

MIMO:一种具有视觉指代多模态输入与像素级定位多模态输出的医学视觉语言模型

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)

Yanyuan Chen, Dexuan Xu, Yu Huang, Songkun Zhan, Hanpin Wang, Dongxue Chen, Xueping Wang, Meikang Qiu, Hang Li

AI总结:

针对医学视觉语言模型输入缺乏视觉线索理解、输出缺乏图像区域关联的问题,提出统一模型MIMO,结合视觉指代输入与像素级定位输出,并构建895K样本的MIMOSeg数据集,实验验证其独特结合了这两种能力。

摘要:

目前,医学视觉语言模型被广泛应用于医学视觉问答任务中。然而,现有模型面临两个问题:在输入方面,模型仅依赖文本指令,缺乏对图像中视觉线索的直接理解;在输出方面,模型仅给出文本答案,缺乏与图像中关键区域的联系。为了解决这些问题,我们提出了一种统一的医学视觉语言模型MIMO,具有视觉指代多模态输入和像素级定位多模态输出。MIMO不仅能结合视觉线索和文本指令来理解复杂的医学图像和语义,还能将文本输出中的医学术语在图像中进行定位。为了克服医学领域相关数据稀缺的问题,我们提出了MIMOSeg,这是一个包含895K样本的综合医学多模态数据集。MIMOSeg从四个不同视角构建,涵盖基本指令跟随以及具有多模态输入和多模态输出的复杂问答。我们在多个下游医学多模态任务上进行了实验。大量实验结果验证了MIMO能够独特地结合视觉指代和像素级定位能力,这是以往模型所不具备的。

英文摘要:

Currently, medical vision language models are widely used in medical vision question answering tasks. However, existing models are confronted with two issues: for input, the model only relies on text instructions and lacks direct understanding of visual clues in the image; for output, the model only gives text answers and lacks connection with key areas in the image. To address these issues, we propose a unified medical vision language model MIMO, with visual referring Multimodal Input and pixel grounding Multimodal Output. MIMO can not only combine visual clues and textual instructions to understand complex medical images and semantics, but can also ground medical terminologies in textual output within the image. To overcome the scarcity of relevant data in the medical field, we propose MIMOSeg, a comprehensive medical multimodal dataset including 895K samples. MIMOSeg is constructed from four different perspectives, covering basic instruction following and complex question answering with multimodal input and multimodal output. We conduct experiments on several downstream medical multimodal tasks. Extensive experimental results verify that MIMO can uniquely combine visual referring and pixel grounding capabilities, which are not available in previous models.

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。

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