1 code implementation • Computing in Cardiology 2023 • Bjørn-Jostein Singstad, Jesper Ravn, Arian Ranjbar
Cardiac arrest (CA) may cause severe brain damage, cognitive impairments and death.
no code implementations • Computing in Cardiology 2023 • Arian Ranjbar, Bjørn-Jostein Singstad, Jesper Ravn, Henrik Schirmer
Medical data such as the electrocardiogram (ECG) has received an increased interest within biometric settings.
1 code implementation • medRxiv 2022 • Bjørn-Jostein Singstad, Belal Tavashi
The best CNN model, using an Inception Time architecture, showed a significant drop in performance, in terms of mean absolute error (MAE), from cross-validation on the training set (7. 90 ± 0. 04 years) to the performance on the test set (8. 3 years).
Ranked #1 on Age Estimation on PhysioNet Challenge 2021
1 code implementation • Computing in Cardiology 2022 • Bjørn-Jostein Singstad, Antony M. Gitau, Markus Kreutzer Johnsen, Johan Ravn, Lars Ailo Bongo, Henrik Schirmer
These sounds are detected by auscultating the heart using a stethoscope, or more recently by a phonocardiogram (PCG).
Ranked #1 on Predict clinical outcome on CirCor DigiScope
no code implementations • techrxiv 2022 • Bjørn-Jostein Singstad
Athletes often have training-induced remodeling of the heart, and this can sometimes be seen as abnormal but non-pathological changes in the electrocardiogram.
1 code implementation • Computing in Cardiology 2022 • Bjørn-Jostein Singstad, Eraraya Morenzo Muten, Pål Haugar Brekke
(3) Finally, CNN models in a classifier chain were trained to classify the remaining 17 diagnoses.
Ranked #2 on ECG Classification on PhysioNet Challenge 2021
1 code implementation • Nordic Machine Intelligence 2021 • Nefeli Panagiota Tzavara, Bjørn-Jostein Singstad
Moreover, we found that pre-training of the models increased the performance in segmenting polyps and endoscopic tools.
Ranked #1 on Medical Image Segmentation on Hyper-Kvasir Dataset (using extra training data)
1 code implementation • Nordic Machine Intelligence 2021 • Steven Hicks, Debesh Jha, Vajira Thambawita, Pål Halvorsen, Bjørn-Jostein Singstad, Sachin Gaur, Klas Pettersen, Morten Goodwin, Sravanthi Parasa, Thomas de Lange, Michael Riegler
MedAI: Transparency in Medical Image Segmentation is a challenge held for the first time at the Nordic AI Meet that focuses on medical image segmentation and transparency in machine learning (ML)-based systems.
1 code implementation • 31 Dec 2020 • Bjørn-Jostein Singstad, Christian Tronstad
Finally, the models were deployed to a Docker image, trained on the provided development data, and tested on the Challenge validation set.
Ranked #1 on ECG Classification on PhysioNet Challenge 2020