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Due to data privacy constraints, data sharing among multiple clinical centersis restricted, which impedes the development of high performance deep learningmodels from multicenter collaboration. Naive weight transfer methods shareintermediate model weights without raw data and hence can bypass data privacyrestrictions. However, performance drops are typically observed when the modelis transferred from one center to the next because of the forgetting problem.Incremental transfer learning, which combines peer-to-peer federated learningand domain incremental learning, can overcome the data privacy issue andmeanwhile preserve model performance by using continual learning techniques. Inthis work, a conventional domain/task incremental learning framework is adaptedfor incremental transfer learning. A comprehensive survey on the efficacy ofdifferent regularization-based continual learning methods for multicentercollaboration is performed. The influences of data heterogeneity, classifierhead setting, network optimizer, model initialization, center order, and weighttransfer type have been investigated thoroughly. Our framework is publiclyaccessible to the research community for further development.

A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration