Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: 1 Online-Ressource.

Language: Englisch

Bibliographic citation: Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging ; volume:4 ; number:1 ; day:14 ; month:3 ; year:2024 ; pages:1-12 ; date:12.2024
Communications medicine ; 4, Heft 1 (14.3.2024), 1-12, 12.2024

Creator: Tayebi Arasteh, Soroosh
Ziller, Alexander
Kuhl, Christiane
Makowski, Marcus
Nebelung, Sven
Braren, Rickmer
Rueckert, Daniel
Truhn, Daniel
Kaissis, Georgios

Contributor: SpringerLink (Online service)

DOI: 10.1038/s43856-024-00462-6

URN: urn:nbn:de:101:1-2405162103071.546116477274

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 11:02 AM CEST

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Tayebi Arasteh, Soroosh
Ziller, Alexander
Kuhl, Christiane
Makowski, Marcus
Nebelung, Sven
Braren, Rickmer
Rueckert, Daniel
Truhn, Daniel
Kaissis, Georgios
SpringerLink (Online service)

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Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging

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