Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: 1 Online-Ressource.

Language: Englisch

Bibliographic citation: Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization ; volume:15 ; number:1 ; day:27 ; month:8 ; year:2024 ; pages:1-19 ; date:12.2024
Nature Communications ; 15, Heft 1 (27.8.2024), 1-19, 12.2024

Creator: Rotem, Oded
Schwartz, Tamar
Maor, Ron
Tauber, Yishay
Shapiro, Maya Tsarfati
Meseguer, Marcos
Gilboa, Daniella
Seidman, Daniel S.
Zaritsky, Assaf

Contributor: SpringerLink (Online service)

DOI: 10.1038/s41467-024-51136-9

URN: urn:nbn:de:101:1-2411130738230.473361832278

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

Last update: 15.08.2025, 7:32 AM CEST

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Rotem, Oded
Schwartz, Tamar
Maor, Ron
Tauber, Yishay
Shapiro, Maya Tsarfati
Meseguer, Marcos
Gilboa, Daniella
Seidman, Daniel S.
Zaritsky, Assaf
SpringerLink (Online service)

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Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization

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