22 citations · 22 across the 1 of their papers we have counts for
6 papers
Robust reconstruction of cardiac T1 maps using RNNs
Nicola Martini, Alessio Vatti, Andrea Ripoli +5
Cardiac magnetic resonance parametric T1 maps are typically reconstructed using non-linear fitting. However this method has limitations due to the high computational cost and robus…
Training of a Skull-Stripping Neural Network with efficient data augmentation
Gabriele Valvano, Nicola Martini, Andrea Leo +4
Skull-stripping methods aim to remove the non-brain tissue from acquisition of brain scans in magnetic resonance (MR) imaging. Although several methods sharing this common purpose…
Unsupervised Data Selection for Supervised Learning
Gabriele Valvano, Andrea Leo, Daniele Della Latta +4
Recent research put a big effort in the development of deep learning architectures and optimizers obtaining impressive results in areas ranging from vision to language processing.…
Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging
Gabriele Valvano, Gianmarco Santini, Nicola Martini +4
Cluster of microcalcifications can be an early sign of breast cancer. In this paper we propose a novel approach based on convolutional neural networks for the detection and segment…
Synthetic contrast enhancement in cardiac CT with Deep Learning
Gianmarco Santini, Lorena M. Zumbo, Nicola Martini +6
In Europe the 20% of the CT scans cover the thoracic region. The acquired images contain information about the cardiovascular system that often remains latent due to the lack of co…
An automatic deep learning approach for coronary artery calcium segmentation
G. Santini, D. Della Latta, N. Martini +6
Coronary artery calcium (CAC) is a significant marker of atherosclerosis and cardiovascular events. In this work we present a system for the automatic quantification of calcium sco…