  Deep learning has become a promising approach for automated medical
diagnoses. When medical data samples are limited, collaboration among multiple
institutions is necessary to achieve high algorithm performance. However,
sharing patient data often has limitations due to technical, legal, or ethical
concerns. In such cases, sharing a deep learning model is a more attractive
alternative. The best method of performing such a task is unclear, however. In
this study, we simulate the dissemination of learning deep learning network
models across four institutions using various heuristics and compare the
results with a deep learning model trained on centrally hosted patient data.
The heuristics investigated include ensembling single institution models,
single weight transfer, and cyclical weight transfer. We evaluated these
approaches for image classification in three independent image collections
(retinal fundus photos, mammography, and ImageNet). We find that cyclical
weight transfer resulted in a performance (testing accuracy = 77.3%) that was
closest to that of centrally hosted patient data (testing accuracy = 78.7%). We
also found that there is an improvement in the performance of cyclical weight
transfer heuristic with high frequency of weight transfer.
