自主监督的文本视觉对齐用于自动脑MRI异常检测:一个多中心研究 (ALIGN研究)
David A Wood1, Emily Guilhem2, Sina Kafiabadi2
1School of Biomedical Engineering and Imaging Sciences, King's College London, Rayne Institute, 4th Floor, Lambeth Wing, London SE17 7EH, UK.
Radiology. Artificial intelligence
|November 26, 2025
概括
一个新的自我监督框架使用放射学报告准确地检测大脑MRI异常,消除了对专家标记数据的需求. 这种方法显示出高诊断性能,并且可以很好地对外部数据集进行概括.
科学领域:
- 医疗成像中的人工智能
- 放射学和医学信息学
- 机器学习用于医疗保健
背景情况:
- 专家标记的数据集对于训练医疗成像中的AI模型至关重要,但成本高且耗时.
- 利用现有的自由文本放射学报告为AI模型开发提供了一个可扩展的替代方案.
- 大脑MRI解释需要专门的知识,使自动检测非常有价值.
研究的目的:
- 开发和验证一个自我监督的文本视觉框架,用于检测大脑MRI异常.
- 通过利用放射学报告,消除对专家注释的数据集的依赖.
- 评估该框架在多个机构的诊断性能和通用性.
主要方法:
- 一项回顾性和前性多中心研究,涉及81,936次脑MRI检查和培训/内部测试报告.
- 开发一种神经放射学语言模型 (NeuroBERT),使用自我监督学习进行报告嵌入.
- 训练卷积神经网络将MRI扫描映射到嵌入物,然后进行文本图像相似度评分以检测异常.
主要成果:
- 该框架实现了正常与异常分类的接收器运行特征曲线 (AUC) 下的面积为0.95.
- 对外部位的优异通用性,检查级AUC从0.85到0.90不等.
- 在零射击分类任务 (平均AUC0.89) 和视觉语义图像检索 (平均精度0.84) 中的高性能.
结论:
- 开发的自我监督的文本视觉框架准确地检测大脑MRI异常.
- 这种方法成功地利用了自由文本放射学报告,消除了对专家标记培训数据的需求.
- 该框架展示了强大的诊断能力和在临床环境中的广泛应用.
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