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Viser treff 1-7 av 7

2021
1 Kuttner, Samuel; Wickstrøm, Kristoffer Knutsen; Lubberink, Mark; Tolf, Andreas; Burman, Joachim; Sundset, Rune; Jenssen, Robert; Appel, Lieuwe; Axelsson, Jan.
Cerebral blood flow measurements with <sup>15</sup>O-water PET using a non-invasive machine-learning-derived arterial input function. Journal of Cerebral Blood Flow and Metabolism 2021 s. 1-13
UiT UNN Untitled
 
2020
2 Wickstrøm, Kristoffer Knutsen; Mikalsen, Karl Øyvind; Kampffmeyer, Michael; Revhaug, Arthur; Jenssen, Robert.
Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series. IEEE journal of biomedical and health informatics 2020
NR UiT UNN Untitled
 
3 Yu, Shujian; Wickstrøm, Kristoffer Knutsen; Jenssen, Robert; Principe, Jose.
Understanding Convolutional Neural Networks With Information Theory: An Initial Exploration. IEEE Transactions on Neural Networks and Learning Systems 2020 ;Volum 32.(1) s. 436-442
UiT Untitled
 
4 Kuttner, Samuel; Wickstrøm, Kristoffer Knutsen; Kalda, Gustav; Dorraji, Seyed Esmaeil; Martin-Armas, Montserrat; Oteiza, Ana; Jenssen, Robert; Fenton, Kristin Andreassen; Sundset, Rune; Axelsson, Jan.
Machine learning derived input-function in a dynamic 18F-FDG PET study of mice. Biomedical Engineering & Physics Express 2020 ;Volum 6:015020. s. 1-13
OUS UiT UNN Untitled
 
5 Kvammen, Andreas; Wickstrøm, Kristoffer Knutsen; McKay, Derek; Partamies, Noora.
Auroral Image Classification With Deep Neural Networks. Journal of Geophysical Research (JGR): Space Physics 2020 ;Volum 125. s. -
NORCE UiB UiT UNIS Untitled
 
2019
6 Wickstrøm, Kristoffer Knutsen; Kampffmeyer, Michael C.; Jenssen, Robert.
Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps. Medical Image Analysis 2019
UiT Untitled
 
2018
7 Wickstrøm, Kristoffer Knutsen; Kampffmeyer, Michael C.; Jenssen, Robert.
Uncertainty modeling and interpretability in convolutional neural networks for polyp segmentation. I: 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE Signal Processing Society 2018 ISBN 978-1-5386-5477-4.
UiT Untitled