NeuroCrypt: Machine Learning Over Encrypted Distributed Neuroimaging Data.

ABSTRACT: The field of neuroimaging can greatly benefit from building machine learning models to detect and predict diseases, and discover novel biomarkers, but much of the data collected at various organizations and research centers is unable to be shared due to privacy or regulatory concerns (especially for clinical data or rare disorders). In addition, aggregating data across multiple large studies results in a huge amount of duplicated technical debt and the resources required can be challenging or impossible for an individual site to build. Training on the data distributed across organizations can result in models that generalize much better than models trained on data from any of organizations alone. While there are approaches for decentralized sharing, these often do not provide the highest p

SUBMITTER: Senanayake N 

PROVIDER: S-EPMC8566325 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature