Zobrazeno 1 - 10
of 93
pro vyhledávání: '"Mikalsen, Karl Øyvind"'
Autor:
Chomutare, Taridzo, Svenning, Therese Olsen, Hernández, Miguel Ángel Tejedor, Ngo, Phuong Dinh, Budrionis, Andrius, Markljung, Kaisa, Hind, Lill Irene, Torsvik, Torbjørn, Mikalsen, Karl Øyvind, Babic, Aleksandar, Dalianis, Hercules
\textbf{Trial design} Crossover randomized controlled trial. \textbf{Methods} An AI tool, Easy-ICD, was developed to assist clinical coders and was tested for improving both accuracy and time in a user study in Norway and Sweden. Participants were ra
Externí odkaz:
http://arxiv.org/abs/2410.23725
Autor:
Fredriksen, Helge, Burman, Per Joel, Woldaregay, Ashenafi, Mikalsen, Karl Øyvind, Nymo, Ståle
Patients being admitted to a hospital will most often be associated with a certain clinical development during their stay. However, there is always a risk of patients being subject to the wrong diagnosis or to a certain treatment not pertaining to th
Externí odkaz:
http://arxiv.org/abs/2311.09165
A clinically motivated self-supervised approach for content-based image retrieval of CT liver images
Autor:
Wickstrøm, Kristoffer Knutsen, Østmo, Eirik Agnalt, Radiya, Keyur, Mikalsen, Karl Øyvind, Kampffmeyer, Michael Christian, Jenssen, Robert
Deep learning-based approaches for content-based image retrieval (CBIR) of CT liver images is an active field of research, but suffers from some critical limitations. First, they are heavily reliant on labeled data, which can be challenging and costl
Externí odkaz:
http://arxiv.org/abs/2207.04812
Autor:
Wickstrøm, Kristoffer, Johnson, J. Emmanuel, Løkse, Sigurd, Camps-Valls, Gustau, Mikalsen, Karl Øyvind, Kampffmeyer, Michael, Jenssen, Robert
This paper presents the kernelized Taylor diagram, a graphical framework for visualizing similarities between data populations. The kernelized Taylor diagram builds on the widely used Taylor diagram, which is used to visualize similarities between po
Externí odkaz:
http://arxiv.org/abs/2205.08864
The lack of labeled data is a key challenge for learning useful representation from time series data. However, an unsupervised representation framework that is capable of producing high quality representations could be of great value. It is key to en
Externí odkaz:
http://arxiv.org/abs/2203.09270
Autor:
Wickstrøm, Kristoffer K., Trosten, Daniel J., Løkse, Sigurd, Boubekki, Ahcène, Mikalsen, Karl Øyvind, Kampffmeyer, Michael C., Jenssen, Robert
Despite the significant improvements that representation learning via self-supervision has led to when learning from unlabeled data, no methods exist that explain what influences the learned representation. We address this need through our proposed a
Externí odkaz:
http://arxiv.org/abs/2112.10161
Autor:
Escudero-Arnanz, Óscar, Rodríguez-Álvarez, Joaquín, Mikalsen, Karl Øyvind, Jenssen, Robert, Soguero-Ruiz, Cristina
The acquisition of Antimicrobial Multidrug Resistance (AMR) in patients admitted to the Intensive Care Units (ICU) is a major global concern. This study analyses data in the form of multivariate time series (MTS) from 3476 patients recorded at the IC
Externí odkaz:
http://arxiv.org/abs/2107.10398
Autor:
Wickstrøm, Kristoffer, Mikalsen, Karl Øyvind, Kampffmeyer, Michael, Revhaug, Arthur, Jenssen, Robert
Deep learning-based support systems have demonstrated encouraging results in numerous clinical applications involving the processing of time series data. While such systems often are very accurate, they have no inherent mechanism for explaining what
Externí odkaz:
http://arxiv.org/abs/2010.11310
A large fraction of the electronic health records (EHRs) consists of clinical measurements collected over time, such as lab tests and vital signs, which provide important information about a patient's health status. These sequences of clinical measur
Externí odkaz:
http://arxiv.org/abs/2002.12359
Autor:
Mikalsen, Karl Øyvind, Soguero-Ruiz, Cristina, Bianchi, Filippo Maria, Revhaug, Arthur, Jenssen, Robert
The time series cluster kernel (TCK) provides a powerful tool for analysing multivariate time series subject to missing data. TCK is designed using an ensemble learning approach in which Bayesian mixture models form the base models. Because of the Ba
Externí odkaz:
http://arxiv.org/abs/1907.05251