增量迁移学习综述：结合对等联邦学习与领域增量学习以实现多中心协作
A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration
AI总结：
本文提出融合对等联邦学习与领域增量学习的增量迁移学习框架，系统评估基于正则化的持续学习方法及多种因素对多中心临床协作的影响。
摘要：
由于数据隐私限制，多个临床中心之间的数据共享受到约束，这阻碍了通过多中心协作开发高性能深度学习模型。朴素的权重迁移方法无需原始数据即可共享中间模型权重，因此能够绕开数据隐私限制。然而，由于遗忘问题，当模型从一个中心迁移到下一个中心时，通常会观察到性能下降。增量迁移学习结合了对等联邦学习与领域增量学习，能够克服数据隐私问题，同时通过使用持续学习技术保持模型性能。在本工作中，一个常规的领域/任务增量学习框架被改造用于增量迁移学习。本文全面综述了不同的基于正则化的持续学习方法在多中心协作中的功效。研究深入考察了数据异质性、分类器头设置、网络优化器、模型初始化、中心顺序以及权重迁移类型的影响。我们的框架已向研究社区公开，以供进一步开发。
英文摘要：
Due to data privacy constraints, data sharing among multiple clinical centers is restricted, which impedes the development of high performance deep learning models from multicenter collaboration. Naive weight transfer methods share intermediate model weights without raw data and hence can bypass data privacy restrictions. However, performance drops are typically observed when the model is transferred from one center to the next because of the forgetting problem. Incremental transfer learning, which combines peer-to-peer federated learning and domain incremental learning, can overcome the data privacy issue and meanwhile preserve model performance by using continual learning techniques. In this work, a conventional domain/task incremental learning framework is adapted for incremental transfer learning. A comprehensive survey on the efficacy of different regularization-based continual learning methods for multicenter collaboration is performed. The influences of data heterogeneity, classifier head setting, network optimizer, model initialization, center order, and weight transfer type have been investigated thoroughly. Our framework is publicly accessible to the research community for further development.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
