  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.
