31 citations · 79 across the 8 of their papers we have counts for
8 papers
Deformable image registration with deep network priors: a study on longitudinal PET images
Constance Fourcade, Ludovic Ferrer, Noemie Moreau +10
Longitudinal image registration is challenging and has not yet benefited from major performance improvements thanks to deep-learning. Inspired by Deep Image Prior, this paper intro…
Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge
Gianmarco Santini, Noémie Moreau, Mathieu Rubeaux
Precise characterization of the kidney and kidney tumor characteristics is of outmost importance in the context of kidney cancer treatment, especially for nephron sparing surgery w…
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…