3 citations · 4 across the 2 of their papers we have counts for
6 papers · 1 filter
QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge
Hongwei Bran Li, Fernando Navarro, Ivan Ezhov +77
Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a sign…
Knowledge-driven deep learning for fast MR imaging: undersampled MR image reconstruction from supervised to un-supervised learning
Shanshan Wang, Ruoyou Wu, Sen Jia +4
Deep learning (DL) has emerged as a leading approach in accelerating MR imaging. It employs deep neural networks to extract knowledge from available datasets and then applies the t…
Modality Exchange Network for Retinogeniculate Visual Pathway Segmentation
Hua Han, Cheng Li, Lei Xie +3
Accurate segmentation of the retinogeniculate visual pathway (RGVP) aids in the diagnosis and treatment of visual disorders by identifying disruptions or abnormalities within the p…
LESEN: Label-Efficient deep learning for Multi-parametric MRI-based Visual Pathway Segmentation
Alou Diakite, Cheng Li, Lei Xie +3
Recent research has shown the potential of deep learning in multi-parametric MRI-based visual pathway (VP) segmentation. However, obtaining labeled data for training is laborious a…
Model-based Federated Learning for Accurate MR Image Reconstruction from Undersampled k-space Data
Ruoyou Wu, Cheng Li, Juan Zou +3
Deep learning-based methods have achieved encouraging performances in the field of magnetic resonance (MR) image reconstruction. Nevertheless, to properly learn a powerful and robu…
SelfCoLearn: Self-supervised collaborative learning for accelerating dynamic MR imaging
Juan Zou, Cheng Li, Sen Jia +4
Lately, deep learning has been extensively investigated for accelerating dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved. However, without fully sampl…