Fully automated quantification of cardiac chamber and function assessment in 2-D echocardiography: clinical feasibility of deep learning-based algorithms

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Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 1573-0743

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: Fully automated quantification of cardiac chamber and function assessment in 2-D echocardiography: clinical feasibility of deep learning-based algorithms ; volume:38 ; number:5 ; day:13 ; month:2 ; year:2022 ; pages:1047-1059 ; date:5.2022
The international journal of cardiovascular imaging ; 38, Heft 5 (13.2.2022), 1047-1059, 5.2022

Creator: Kim, Sekeun
Park, Hyung-Bok
Jeon, Jaeik
Arsanjani, Reza
Heo, Ran
Lee, Sang-Eun
Moon, Inki
Yoo, Sun Kook
Chang, Hyuk-Jae

Contributor: SpringerLink (Online service)

DOI: 10.1007/s10554-021-02482-y

URN: urn:nbn:de:101:1-2022071010160604751650

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

Last update: 21.01.2023, 7:58 AM CET

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Kim, Sekeun
Park, Hyung-Bok
Jeon, Jaeik
Arsanjani, Reza
Heo, Ran
Lee, Sang-Eun
Moon, Inki
Yoo, Sun Kook
Chang, Hyuk-Jae
SpringerLink (Online service)

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Fully automated quantification of cardiac chamber and function assessment in 2-D echocardiography: clinical feasibility of deep learning-based algorithms

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Other Objects (12)

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