17 citations · 46 across the 7 of their papers we have counts for
15 papers
Few-shot Unsupervised Domain Adaptation for Multi-modal Cardiac Image Segmentation
Mingxuan Gu, Sulaiman Vesal, Ronak Kosti +1
Unsupervised domain adaptation (UDA) methods intend to reduce the gap between source and target domains by using unlabeled target domain and labeled source domain data, however, in…
Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimisation for Multi-modal Cardiac Image Segmentation
Sulaiman Vesal, Mingxuan Gu, Ronak Kosti +2
Deep learning models are sensitive to domain shift phenomena. A model trained on images from one domain cannot generalise well when tested on images from a different domain, despit…
Spatio-temporal Multi-task Learning for Cardiac MRI Left Ventricle Quantification
Sulaiman Vesal, Mingxuan Gu, Andreas Maier +1
Quantitative assessment of cardiac left ventricle (LV) morphology is essential to assess cardiac function and improve the diagnosis of different cardiovascular diseases. In current…
A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Zhaohan Xiong, Qing Xia, Zhiqiang Hu +41
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial fir…
The Effect of Data Augmentation on Classification of Atrial Fibrillation in Short Single-Lead ECG Signals Using Deep Neural Networks
Faezeh Nejati Hatamian, Nishant Ravikumar, Sulaiman Vesal +3
Cardiovascular diseases are the most common cause of mortality worldwide. Detection of atrial fibrillation (AF) in the asymptomatic stage can help prevent strokes. It also improves…
COPD Classification in CT Images Using a 3D Convolutional Neural Network
Jalil Ahmed, Sulaiman Vesal, Felix Durlak +4
Chronic obstructive pulmonary disease (COPD) is a lung disease that is not fully reversible and one of the leading causes of morbidity and mortality in the world. Early detection a…