Distributed deep learning networks among institutions for medical imaging.

ABSTRACT:

Objective

Deep learning has become a promising approach for automated support for clinical diagnosis. 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 this study, we propose methods of distributing deep learning models as an attractive alternative to sharing patient data.

Methods

We simulate the distribution of deep learning models across 4 institutions using various training heuristics and compare the results with a deep learning model trained on centrally hosted patient data. The training heuristics investigated include ensembling single institution models, single weight transfer, and cyclic

SUBMITTER: Chang K 

PROVIDER: S-EPMC6077811 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature