Zobrazeno 1 - 10
of 530
pro vyhledávání: '"A.-L. Hammer"'
Autor:
Mats Tveter, Thomas Tveitstøl, Christoffer Hatlestad-Hall, Ana S. Pérez T., Erik Taubøll, Anis Yazidi, Hugo L. Hammer, Ira R. J. Hebold Haraldsen
Publikováno v:
Brain Informatics, Vol 11, Iss 1, Pp 1-12 (2024)
Abstract Deep Learning (DL) has the potential to enhance patient outcomes in healthcare by implementing proficient systems for disease detection and diagnosis. However, the complexity and lack of interpretability impede their widespread adoption in c
Externí odkaz:
https://doaj.org/article/ac8e752216de448587ab16a344df19ac
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-7 (2024)
Abstract Clouds are important factors when projecting future climate. Unfortunately, future cloud fractional cover (the portion of the sky covered by clouds) is associated with significant uncertainty, making climate projections difficult. In this pa
Externí odkaz:
https://doaj.org/article/d4e1a83a39c9446da4c0ad2a14bfb2b5
Autor:
Rachel C. Avard, Megan L. Broad, Fereshteh Zandkarimi, Alexander J. Devanny, Joseph L. Hammer, Karen Yu, Asja Guzman, Laura J. Kaufman
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-18 (2023)
Abstract Multicellular tumor spheroids embedded in collagen I matrices are common in vitro systems for the study of solid tumors that reflect the physiological environment and complexities of the in vivo environment. While collagen I environments are
Externí odkaz:
https://doaj.org/article/2a3fb023fa5049ce85739b32a3a53e4a
Autor:
Thomas Tveitstøl, Mats Tveter, Ana S. Pérez T., Christoffer Hatlestad-Hall, Anis Yazidi, Hugo L. Hammer, Ira R. J. Hebold Haraldsen
Publikováno v:
Frontiers in Neuroinformatics, Vol 17 (2024)
IntroductionA challenge when applying an artificial intelligence (AI) deep learning (DL) approach to novel electroencephalography (EEG) data, is the DL architecture's lack of adaptability to changing numbers of EEG channels. That is, the number of ch
Externí odkaz:
https://doaj.org/article/8216ec604ec042b3b665f49ae8bb0927
Autor:
Vajira Thambawita, Steven A. Hicks, Andrea M. Storås, Thu Nguyen, Jorunn M. Andersen, Oliwia Witczak, Trine B. Haugen, Hugo L. Hammer, Pål Halvorsen, Michael A. Riegler
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-8 (2023)
Abstract A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view. To obtain correct results, manual evaluation requires extensive training. Therefore, compute
Externí odkaz:
https://doaj.org/article/9cd23279754a4c9c93a974c095dd524a
Publikováno v:
Algorithms, Vol 17, Iss 3, p 120 (2024)
Generative models have recently received a lot of attention. However, a challenge with such models is that it is usually not possible to compute the likelihood function, which makes parameter estimation or training of the models challenging. The most
Externí odkaz:
https://doaj.org/article/76cd27c839b346e99189e39e528300af
Publikováno v:
Diagnostics, Vol 13, Iss 22, p 3413 (2023)
An important part of diagnostics is to gain insight into properties that characterize a disease. Machine learning has been used for this purpose, for instance, to identify biomarkers in genomics. However, when patient data are presented as images, id
Externí odkaz:
https://doaj.org/article/b6b30f275aea42c3a46ff72769ddd4ef
Autor:
Vajira Thambawita, Jonas L. Isaksen, Steven A. Hicks, Jonas Ghouse, Gustav Ahlberg, Allan Linneberg, Niels Grarup, Christina Ellervik, Morten Salling Olesen, Torben Hansen, Claus Graff, Niels-Henrik Holstein-Rathlou, Inga Strümke, Hugo L. Hammer, Mary M. Maleckar, Pål Halvorsen, Michael A. Riegler, Jørgen K. Kanters
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
Abstract Recent global developments underscore the prominent role big data have in modern medical science. But privacy issues constitute a prevalent problem for collecting and sharing data between researchers. However, synthetic data generated to rep
Externí odkaz:
https://doaj.org/article/083a56ecd10843639b0fa258ef14862d
Autor:
Yasmine Rebai, Lysett Wagner, Mayssa Gnaien, Merle L. Hammer, Mario Kapitan, Maria Joanna Niemiec, Wael Mami, Amor Mosbah, Erij Messadi, Helmi Mardassi, Slavena Vylkova, Ilse D. Jacobsen, Sadri Znaidi
Publikováno v:
Microorganisms, Vol 11, Iss 8, p 1929 (2023)
Candida albicans is a pathobiont of the gastrointestinal tract. It can contribute to the diversity of the gut microbiome without causing harmful effects. When the immune system is compromised, C. albicans can damage intestinal cells and cause invasiv
Externí odkaz:
https://doaj.org/article/95ef2d6f969a469b8f4c01e0f2cd356c
Autor:
Pia H. Smedsrud, Vajira Thambawita, Steven A. Hicks, Henrik Gjestang, Oda Olsen Nedrejord, Espen Næss, Hanna Borgli, Debesh Jha, Tor Jan Derek Berstad, Sigrun L. Eskeland, Mathias Lux, Håvard Espeland, Andreas Petlund, Duc Tien Dang Nguyen, Enrique Garcia-Ceja, Dag Johansen, Peter T. Schmidt, Ervin Toth, Hugo L. Hammer, Thomas de Lange, Michael A. Riegler, Pål Halvorsen
Publikováno v:
Scientific Data, Vol 8, Iss 1, Pp 1-10 (2021)
Measurement(s) Gastrointestinal Tract • gastrointestinal system disease Technology Type(s) Capsule Endoscope • visual assessment of in vivo video recording Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment alimenta
Externí odkaz:
https://doaj.org/article/a87ce99b976f44c5949b4d3cc76acbe4