开发一个保护隐私的深度学习模型来检测青光眼:一个多中心研究与联合学习
An Ran Ran1, Xi Wang2,3,4, Poemen P Chan1,5
1Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.
The British journal of ophthalmology
|October 19, 2023
概括
联合学习 (FL) 允许使用光学连贯断层扫描 (OCT) 图像进行隐私保护的青光眼的检测. 这种方法确保了数据安全,同时在多个中心实现了可泛化的性能.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 深度学习 (DL) 对青光眼的检测非常有前途.
- 患者隐私和数据安全是将数据集中用于DL模型的重大挑战.
- 使用联合学习 (FL) 开发了一种保护隐私的DL模型,用于从光学连贯断层扫描 (OCT) 图像中检测玻璃眼.
研究的目的:
- 开发和评估一个保护隐私的联合学习 (FL) 模型,用于使用OCT图像检测青光眼.
- 在多中心AI模型开发中解决患者隐私和数据安全问题.
- 评估FL模型在不同眼睛中心和数据集中的通用性和稳定性.
主要方法:
- 这是一项多中心研究,涉及香港,美国和新加坡的七个眼科中心.
- 利用联合学习 (FL) 范式,在每个中心使用中央服务器和本地模型培训.
- 采用FedProx算法进行模型参数聚合和再分配,实验三维网络并对未见的数据集进行测试.
主要成果:
- 飞行模型在七个中心展示了一致的性能,精度从78.3%到98.5%.
- 在两个未见的数据集上观察到稳定的性能,准确度在81.3%至87.8%之间.
- 与传统模型相比,FL模型实现了非劣质的性能,并且表现优于单个中心模型.
结论:
- 3D FL模型有效地利用了无需数据交换的多中心数据集,确保了患者的隐私和数据安全.
- 这种基于OCT的FL范式表明了人工智能在玻璃眼瘤检测中的现实应用的可行途径.
- 这项研究强调了联合学习在眼科人工智能发展方面的潜力.
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