Yan Bingjie, Cao Danmin, Jiang Xinlong, Chen Yiqiang, Dai Weiwei, Dong Fan, Huang Wuliang, Zhang Teng, Gao Chenlong, Chen Qian, Yan Zhen, Wang Zhirui
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
Beijing Key Laboratory of Mobile Computing and Pervasive Device, Beijing, China.
Patterns (N Y). 2024 Feb 2;5(2):100928. doi: 10.1016/j.patter.2024.100928. eCollection 2024 Feb 9.
Data-driven machine learning, as a promising approach, possesses the capability to build high-quality, exact, and robust models from ophthalmic medical data. Ophthalmic medical data, however, presently exist across disparate data silos with privacy limitations, making centralized training challenging. While ophthalmologists may not specialize in machine learning and artificial intelligence (AI), considerable impediments arise in the associated realm of research. To address these issues, we design and develop FedEYE, a scalable and flexible end-to-end ophthalmic federated learning platform. During FedEYE design, we adhere to four fundamental design principles, ensuring that ophthalmologists can effortlessly create independent and federated AI research tasks. Benefiting from the design principles and architecture of FedEYE, it encloses numerous key features, including rich and customizable capabilities, separation of concerns, scalability, and flexible deployment. We also validated the applicability of FedEYE by employing several prevalent neural networks on ophthalmic disease image classification tasks.
数据驱动的机器学习作为一种很有前景的方法，有能力从眼科医学数据构建高质量、准确且强大的模型。然而，眼科医学数据目前存在于分散的数据孤岛中，且有隐私限制，这使得集中式训练具有挑战性。虽然眼科医生可能不专门从事机器学习和人工智能（AI），但在相关研究领域仍存在相当大的障碍。为了解决这些问题，我们设计并开发了FedEYE，一个可扩展且灵活的端到端眼科联邦学习平台。在FedEYE设计过程中，我们遵循四个基本设计原则，确保眼科医生能够轻松创建独立的联邦AI研究任务。受益于FedEYE的设计原则和架构，它具有许多关键特性，包括丰富且可定制的功能、关注点分离、可扩展性和灵活部署。我们还通过在眼科疾病图像分类任务中使用几种流行的神经网络验证了FedEYE的适用性。
