Zobrazeno 1 - 10
of 450
pro vyhledávání: '"Hammer, P. L."'
Missing data is a prevalent issue that can significantly impair model performance and interpretability. This paper briefly summarizes the development of the field of missing data with respect to Explainable Artificial Intelligence and experimentally
Externí odkaz:
http://arxiv.org/abs/2407.00411
Understanding sleep and activity patterns plays a crucial role in physical and mental health. This study introduces a novel approach for sleep detection using weakly supervised learning for scenarios where reliable ground truth labels are unavailable
Externí odkaz:
http://arxiv.org/abs/2402.17601
Autor:
Thambawita, Vajira, Hicks, Steven A., Storås, Andrea M., Nguyen, Thu, Andersen, Jorunn M., Witczak, Oliwia, Haugen, Trine B., Hammer, Hugo L., Halvorsen, Pål, Riegler, Michael A.
Publikováno v:
Sci Data 10, 260 (2023)
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, computer-assiste
Externí odkaz:
http://arxiv.org/abs/2212.02842
Missing data is a commonly occurring problem in practice. Many imputation methods have been developed to fill in the missing entries. However, not all of them can scale to high-dimensional data, especially the multiple imputation techniques. Meanwhil
Externí odkaz:
http://arxiv.org/abs/2205.15150
Autor:
Storås, Andrea M., Strümke, Inga, Riegler, Michael A., Grauslund, Jakob, Hammer, Hugo L., Yazidi, Anis, Halvorsen, Pål, Gundersen, Kjell G., Utheim, Tor P., Jackson, Catherine
Dry eye disease (DED) has a prevalence of between 5 and 50\%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in th
Externí odkaz:
http://arxiv.org/abs/2109.01658
Autor:
Thambawita, Vajira, Salehi, Pegah, Sheshkal, Sajad Amouei, Hicks, Steven A., Hammer, Hugo L., Parasa, Sravanthi, de Lange, Thomas, Halvorsen, Pål, Riegler, Michael A.
Analyzing medical data to find abnormalities is a time-consuming and costly task, particularly for rare abnormalities, requiring tremendous efforts from medical experts. Artificial intelligence has become a popular tool for the automatic processing o
Externí odkaz:
http://arxiv.org/abs/2107.00471
Electroencephalograms (EEG) are noninvasive measurement signals of electrical neuronal activity in the brain. One of the current major statistical challenges is formally measuring functional dependency between those complex signals. This paper, propo
Externí odkaz:
http://arxiv.org/abs/2105.06418
This paper introduces a new time-frequency representation method for biomedical signals: the dyadic aggregated autoregressive (DASAR) model. Signals, such as electroencephalograms (EEGs) and functional near-infrared spectroscopy (fNIRS), exhibit phys
Externí odkaz:
http://arxiv.org/abs/2105.10406
The complex-pole frequency representation (COFRE) is introduced in this paper as a new approach for spectrum modeling in biomedical signals. Our method allows us to estimate the spectral power density at precise frequencies using an array of narrow b
Externí odkaz:
http://arxiv.org/abs/2105.13476
For incremental quantile estimators the step size and possibly other tuning parameters must be carefully set. However, little attention has been given on how to set these values in an online manner. In this article we suggest two novel procedures tha
Externí odkaz:
http://arxiv.org/abs/2004.12588