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Harnessing feature extraction capacities from a pre-trained..:
Lachmann, Mark
;
Rippen, Elena
;
Rueckert, Daniel
...
European Heart Journal - Digital Health. 3 (2022) 2 - p. 153-168 , 2022
Link:
https://doi.org/10.1093/ehjdh/ztac004
RT Journal T1
Harnessing feature extraction capacities from a pre-trained convolutional neural network (VGG-16) for the unsupervised distinction of aortic outflow velocity profiles in patients with severe aortic stenosis
UL https://suche.suub.uni-bremen.de/peid=cr-10.1093_ehjdh_ztac004&Exemplar=1&LAN=DE A1 Lachmann, Mark A1 Rippen, Elena A1 Rueckert, Daniel A1 Schuster, Tibor A1 Xhepa, Erion A1 von Scheidt, Moritz A1 Pellegrini, Costanza A1 Trenkwalder, Teresa A1 Rheude, Tobias A1 Stundl, Anja A1 Thalmann, Ruth A1 Harmsen, Gerhard A1 Yuasa, Shinsuke A1 Schunkert, Heribert A1 Kastrati, Adnan A1 Joner, Michael A1 Kupatt, Christian A1 Laugwitz, Karl Ludwig PB Oxford University Press (OUP) YR 2022 SN 2634-3916 JF European Heart Journal - Digital Health VO 3 IS 2 SP 153 OP 168 LK http://dx.doi.org/https://doi.org/10.1093/ehjdh/ztac004 DO https://doi.org/10.1093/ehjdh/ztac004 SF ELIB - SuUB Bremen
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