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Document type:
Article
Author(s):
Kaissis, Georgios; Ziller, Alexander; Passerat-Palmbach, Jonathan; Ryffel, Theo; Usynin, Dmitrii; Trask, Andrew; Lima, Ionesio; Mancuso, Jason; Jungmann, Friederike; Steinborn, Marc-Matthias; Saleh, Andreas; Makowski, Marcus; Rueckert, Daniel; Braren, Rickmer
Title:
End-to-end privacy preserving deep learning on multi-institutional medical imaging
Abstract:
Using large, multi-national datasets for high-performance medical imaging AI systems requires innovation in privacy-preserving machine learning so models can train on sensitive data without requiring data transfer. Here we present PriMIA (Privacy-preserving Medical Image Analysis), a free, open-source software framework for differentially private, securely aggregated federated learning and encrypted inference on medical imaging data. We test PriMIA using a real-life case study in which an expert...     »
Journal title abbreviation:
Nat. Mach. Intell.
Year:
2021
Journal volume:
3
Journal issue:
6
Pages contribution:
473-484
Fulltext / DOI:
doi:10.1038/s42256-021-00337-8
TUM Institution:
Institut für Diagnostische und Interventionelle Radiologie; Institut für Medizinische Statistik und Epidemiologie
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