126 citations · 241 across the 10 of their papers we have counts for
11 papers
Advancing sleep detection by modelling weak label sets: A novel weakly supervised learning approach
Matthias Boeker, Vajira Thambawita, Michael Riegler +2
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 lea…
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…
SinGAN-Seg: Synthetic training data generation for medical image segmentation
Vajira Thambawita, Pegah Salehi, Sajad Amouei Sheshkal +6
Analyzing medical data to find abnormalities is a time-consuming and costly task, particularly for rare abnormalities, requiring tremendous efforts from medical experts. Artificial…
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…
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…
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…
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…
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…
Stacked dense optical flows and dropout layers to predict sperm motility and morphology
Vajira Thambawita, Pål Halvorsen, Hugo Hammer +2
In this paper, we analyse two deep learning methods to predict sperm motility and sperm morphology from sperm videos. We use two different inputs: stacked pure frames of videos and…
Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction
Steven A. Hicks, Jorunn M. Andersen, Oliwia Witczak +5
Methods for automatic analysis of clinical data are usually targeted towards a specific modality and do not make use of all relevant data available. In the field of male human repr…
The Medico-Task 2018: Disease Detection in the Gastrointestinal Tract using Global Features and Deep Learning
Vajira Thambawita, Debesh Jha, Michael Riegler +4
In this paper, we present our approach for the 2018 Medico Task classifying diseases in the gastrointestinal tract. We have proposed a system based on global features and deep neur…