Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Andreas Østvik"'
Autor:
Sebastien Muller, Håkon Abildsnes, Andreas Østvik, Oda Kragset, Inger Gangås, Harriet Birke, Thomas Langø, Carl-Jørgen Arum
Publikováno v:
European Urology Open Science, Vol 27, Iss , Pp 33-42 (2021)
Background: Extracorporeal shock wave lithotripsy (ESWL) of kidney stones is losing ground to more expensive and invasive endoscopic treatments. Objective: This proof-of-concept project was initiated to develop artificial intelligence (AI)-augmented
Externí odkaz:
https://doaj.org/article/9444f501d65e44af8e58310a063f6a02
Autor:
Ivar M. Salte, Andreas Østvik, Sindre H. Olaisen, Sigve Karlsen, Thomas Dahlslett, Erik Smistad, Torfinn K. Eriksen-Volnes, Harald Brunvand, Kristina H. Haugaa, Thor Edvardsen, Håvard Dalen, Lasse Lovstakken, Bjørnar Grenne
Publikováno v:
Journal of the American Society of Echocardiography
Aims Assessment of left ventricular (LV) function by echocardiography is hampered by modest test-retest reproducibility. A novel artificial intelligence (AI) method based on deep learning provides fully automated measurements of LV global longitudina
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c94a87ce8ba13da6fe08ce0f53a05b6a
https://doi.org/10.1016/j.echo.2023.02.017
https://doi.org/10.1016/j.echo.2023.02.017
Autor:
David Pasdeloup, Sindre H. Olaisen, Andreas Østvik, Sigbjorn Sabo, Håkon N. Pettersen, Espen Holte, Bjørnar Grenne, Stian B. Stølen, Erik Smistad, Svein Arne Aase, Håvard Dalen, Lasse Løvstakken
Publikováno v:
Ultrasound in Medicine and Biology
Measurements of cardiac function such as left ventricular ejection fraction and myocardial strain are typically based on 2D ultrasound imaging. The reliability of these measurements strongly depends on the correct pose of the transducer such that the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2dfe96559cc22f41379708038ead477c
https://hdl.handle.net/11250/3063685
https://hdl.handle.net/11250/3063685
High Performance Neural Network Inference, Streaming, and Visualization of Medical Images Using FAST
Publikováno v:
IEEE Access, Vol 7, Pp 136310-136321 (2019)
Deep convolutional neural networks have quickly become the standard for medical image analysis. Although there are many frameworks focusing on training neural networks, there are few that focus on high performance inference and visualization of medic
Externí odkaz:
https://doaj.org/article/7f94cd5f48d64e30a795064b0a31bd42