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默林:一个计算机断层扫描视觉语言基础模型和数据集.
Louis Blankemeier1,2,3, Ashwin Kumar2,3, Joseph Paul Cohen2,3
1Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
Nature
|March 4, 2026
概括
默林是一种新的3D视觉语言模型 (VLM),通过整合体积扫描,电子健康记录和放射学报告来增强腹部CT扫描分析. 这种先进的VLM在诊断,预后和质量任务中表现出卓越的性能,有助于放射科医生和生物标志物发现.
科学领域:
- 医疗成像中的人工智能
- 放射学和医学诊断 医学诊断
- 计算机视觉和自然语言处理
背景情况:
- 由于放射科医生短缺,越来越多的腹部计算机断层扫描 (CT) 扫描需要自动化分析工具.
- 医疗成像的现有视觉语言模型 (VLM) 通常仅限于2D数据和简短的报告,阻碍了全面的腹部CT解释.
研究的目的:
- 推出Merlin,3D VLM,旨在进行深入的腹部CT解释,克服以前基于2D的方法的局限性.
- 开发一种能够从体积CT扫描,电子健康记录和无手动注释的放射学报告中学习的VLM.
主要方法:
- 开发了Merlin,一个3DVLM,利用一个大型临床数据集的多阶段预训练框架 (>600万图像,>180万诊断代码,>600万报告令牌).
- 在6个任务类型和752个单个任务上评估Merlin,包括零射击分类,交叉模式检索,慢性疾病预测,报告生成和3D语义细分.
- 通过广泛的内部 (5,137次扫描) 和外部测试 (44,098次扫描) 在多个机构和公共数据集中验证了Merlin.
主要成果:
- 默林在机构和解剖学上表现出高度的概括性,超过现有的2DVLM,CT基础模型和现成的放射学模型.
- 在30个发现和692个表型的零射击分类和有效的跨模式检索方面取得了强的表现.
- 展示了适应任务的熟练程度,包括6种疾病的5年慢性疾病预测,放射学报告生成和20个器官的3D语义细分.
结论:
- 默林代表了自动腹部CT解释的重大进步,协助放射科医生和减轻工作量.
- 3D VLM具有生物标志物发现和疾病风险分层的潜力,超出诊断任务的附加值.
- 经过训练的模型,代码和数据集 (25,494对) 的发布有助于进一步研究和开发医疗AI.
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