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eess.IV2023★ 2 cited
LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus Images
Jonathan Fhima, Jan Van Eijgen, Hana Kulenovic +7
The retina is the only part of the human body in which blood vessels can be accessed non-invasively using imaging techniques such as digital fundus images (DFI). The spatial distri…
eess.IV2023
PCMC-T1: Free-breathing myocardial T1 mapping with Physically-Constrained Motion Correction
Eyal Hanania, Ilya Volovik, Lilach Barkat +2
T1 mapping is a quantitative magnetic resonance imaging (qMRI) technique that has emerged as a valuable tool in the diagnosis of diffuse myocardial diseases. However, prevailing ap…
eess.IV2022
NPB-REC: Non-parametric Assessment of Uncertainty in Deep-learning-based MRI Reconstruction from Undersampled Data
Samah Khawaled, Moti Freiman
Uncertainty quantification in deep-learning (DL) based image reconstruction models is critical for reliable clinical decision making based on the reconstructed images. We introduce…