17 citations · 17 across the 4 of their papers we have counts for
4 papers
Noise2Contrast: Multi-Contrast Fusion Enables Self-Supervised Tomographic Image Denoising
Fabian Wagner, Mareike Thies, Laura Pfaff +10
Self-supervised image denoising techniques emerged as convenient methods that allow training denoising models without requiring ground-truth noise-free data. Existing methods usual…
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…