activity
20182021
most citedExtracting temporal features into a spatial domain using autoencoders for sperm video analysis

8 citations · 12 across the 3 of their papers we have counts for

collaborators

9 papers

cs.LG2021

Artificial Intelligence in Dry Eye Disease

Andrea M. Storås, Inga Strümke, Michael A. Riegler +7

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 underdiagnos…

q-bio.QM2021

The Complex-Pole Filter Representation (COFRE) for spectral modeling of fNIRS signals

Marco A. Pinto Orellana, Peyman Mirtaheri, Hugo L. Hammer

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 sp…

eess.SP2021

Dyadic aggregated autoregressive (DASAR) model for time-frequency representation of biomedical signals

Marco A. Pinto-Orellana, Habib Sherkat, Peyman Mirtaheri +1

This paper introduces a new time-frequency representation method for biomedical signals: the dyadic aggregated autoregressive (DASAR) model. Signals, such as electroencephalograms…

stat.ME2021

SCAU: Modeling spectral causality for multivariate time series with applications to electroencephalograms

Marco Antonio Pinto-Orellana, Peyman Mirtaheri, Hugo L. Hammer +1

Electroencephalograms (EEG) are noninvasive measurement signals of electrical neuronal activity in the brain. One of the current major statistical challenges is formally measuring…

cs.LG2020

An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning applied to Gastrointestinal Tract Abnormality Classification

Vajira Thambawita, Debesh Jha, Hugo Lewi Hammer +4

Precise and efficient automated identification of Gastrointestinal (GI) tract diseases can help doctors treat more patients and improve the rate of disease detection and identifica…

cs.CV20198 cited

Extracting temporal features into a spatial domain using autoencoders for sperm video analysis

Vajira Thambawita, Pål Halvorsen, Hugo Hammer +2

In this paper, we present a two-step deep learning method that is used to predict sperm motility and morphology-based on video recordings of human spermatozoa. First, we use an aut…