Collaborative Privacy-preserving Approaches for Distributed Deep Learning Using Multi-Institutional Data-Reference-Cited by-同舟云学术

Collaborative Privacy-preserving Approaches for Distributed Deep Learning Using Multi-Institutional Data

Published:2023-04-01 Issue:4 Volume:43 Page:
ISSN:0271-5333
Container-title:RadioGraphics
language:en
Short-container-title:RadioGraphics

Author:

Gupta Sharut¹^ORCID,Kumar Sourav¹,Chang Ken¹,Lu Charles¹,Singh Praveer¹,Kalpathy-Cramer Jayashree¹^ORCID

Affiliation:

1. From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 13th Street, Building 149, Room 2301, Charlestown, MA 02129 (S.G., S.K., K.C., C.L., P.S., J.K.C.); and Indian Institute of Technology Delhi, New Delhi, India (S.G., S.K.).

Funder

National Institutes of Health

National Science Foundation

Publisher

Radiological Society of North America (RSNA)

Subject

Radiology, Nuclear Medicine and imaging

Link

http://pubs.rsna.org/doi/pdf/10.1148/rg.220107

Reference33 articles.

1. Deep Learning: A Primer for Radiologists

2. Geographic Distribution of US Cohorts Used to Train Deep Learning Algorithms