Search Results for author:
Found 9 papers, 7 papers with code
MemoryInception: Predicting Neurological Recovery from EEG Using Recurrent Inceptions
Cardiac arrest (CA) may cause severe brain damage, cognitive impairments and death.
Towards Invariant Soft Biometrics from Electrocardiograms
Medical data such as the electrocardiogram (ECG) has received an increased interest within biometric settings.
Using deep convolutional neural networks to predict patients age based on ECGs from an independent test cohort
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
Phonocardiogram Classification Using 1-Dimensional Inception Time Convolutional Neural Networks
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
Norwegian Endurance Athlete ECG Database
Athletes often have training-induced remodeling of the heart, and this can sometimes be seen as abnormal but non-pathological changes in the electrocardiogram.
Multi-Label ECG Classification Using Convolutional Neural Networks in a Classifier Chain
(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
Transfer Learning in Polyp and Endoscopic Tool Segmentation from Colonoscopy Images
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)
MedAI: Transparency in Medical Image Segmentation
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.
Convolutional Neural Network and Rule-Based Algorithms for Classifying 12-lead ECGs
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
