Encrypted federated learning for secure decentralized collaboration in cancer image analysis

Truhn, D., Arasteh, S.T., Saldanha, O.L. et al. (24 more authors) (2024) Encrypted federated learning for secure decentralized collaboration in cancer image analysis. Medical Image Analysis, 92. 103059. ISSN 1361-8415

Abstract

Metadata

Item Type: Article
Authors/Creators:
  • Truhn, D.
  • Arasteh, S.T.
  • Saldanha, O.L.
  • Mueller-Franzes, G.
  • Khader, F.
  • Quirke, P. ORCID logo https://orcid.org/0000-0002-3597-5444
  • West, N.P. ORCID logo https://orcid.org/0000-0002-0346-6709
  • Gray, R.
  • Hutchins, G.G.A.
  • James, J.A.
  • Loughrey, M.B.
  • Salto-Tellez, M.
  • Brenner, H.
  • Brobeil, A.
  • Yuan, T.
  • Chang-Claude, J.
  • Hoffmeister, M.
  • Foersch, S.
  • Han, T.
  • Keil, S.
  • Schulze-Hagen, M.
  • Isfort, P.
  • Bruners, P.
  • Kaissis, G.
  • Kuhl, C.
  • Nebelung, S.
  • Kather, J.N.
Copyright, Publisher and Additional Information:

© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Keywords: Federated learning; Homomorphic encryption; Histopathology; Radiology; Artificial intelligence; Privacy-preserving deep learning
Dates:
  • Published: February 2024
  • Published (online): 7 December 2023
  • Accepted: 5 December 2023
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Medical Research (LIMR) > Division of Pathology and Data Analytics
Depositing User: Symplectic Publications
Date Deposited: 20 Jun 2024 16:03
Last Modified: 20 Jun 2024 16:03
Status: Published
Publisher: Elsevier
Identification Number: 10.1016/j.media.2023.103059
Related URLs:
Open Archives Initiative ID (OAI ID):

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