A deep learning approach to predict collateral flow in stroke patients using radiomic features from perfusion images

Benutzer: Gast

Dokumenttyp:: Zeitschriftenaufsatz
Autor(en):: Tetteh, Giles ; Navarro, Fernando ; Meier, Raphael ; Kaesmacher, Johannes ; Paetzold, Johannes C. ; Kirschke, Jan S. ; Zimmer, Claus ; Wiest, Roland ; Menze, Bjoern H.
Titel:: A deep learning approach to predict collateral flow in stroke patients using radiomic features from perfusion images
Stichworte:: Neurology ; collateral flow ; radiomics ; perfusion ; reinforcement learning ; image descriptors ; angiography ; auto-encoder ; deep learning
Zeitschriftentitel:: Frontiers in Neurology
Jahr:: 2023
Band / Volume:: 14
Volltext / DOI:: doi:10.3389/fneur.2023.1039693
Verlag / Institution:: Frontiers Media S.A.
E-ISSN:: 1664-2295
Publikationsdatum:: 21.02.2023
CC-Lizenz:: by, https://creativecommons.org/licenses/by/4.0

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