从预训练到隐私:联合超声波基础模型与自我监督的学习
Yuncheng Jiang1,2,3,4, Chun-Mei Feng5, Jinke Ren1,2
1FNii-Shenzhen, The Chinese University of Hong Kong, Shenzhen, Shenzhen, 518172, China.
NPJ digital medicine
|November 21, 2025
概括
一个新的AI模型UltraFedFM通过从19个器官的100多万张图像中学习来增强超声波诊断,而不会影响患者的隐私. 它与专家超声波仪在疾病诊断和病变细分方面的性能相匹配.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 联合学习学习 (Federated Learning) 是一种联合学习.
背景情况:
- 传统的超声波诊断依赖于专业知识,面临图像质量限制和潜在的错误.
- 超声波的现有人工智能需要大量的标记数据,造成隐私风险,并且由于特定任务的设计,缺乏广泛的实用性.
研究的目的:
- 介绍UltraFedFM,一个保护隐私的超声波基础模型,解决医疗成像当前人工智能的局限性.
- 为了在多个机构中利用联合学习进行协作,安全的AI模型培训.
主要方法:
- 在9个国家的16个机构中使用联邦学习进行UltraFedFM的合作预培训.
- 利用一个多样化的数据集,包括超过100万张超声波图像,涵盖19个器官和10种模式.
- 实施安全的培训框架,以确保患者的隐私.
主要成果:
- UltraFedFM在疾病诊断方面实现了0.927的平均AUROC,在病变细分方面达到0.878的DSC.
- 该模型在诊断8种常见的全身疾病方面超过了中级超声波仪,并与专家级超声波仪相匹配.
- 在各种器官和超声波模式中表现出强大的概括和诊断能力.
结论:
- UltraFedFM显著提高了临床超声波诊断,同时保持了患者的隐私.
- 基础模型代表了AI驱动的医学成像在临床实践中的重大进步.
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