activity
20082022
most citedAutomatic quantification of the LV function and mass: a deep learning approach for cardiovascular MRI

39 citations · 51 across the 7 of their papers we have counts for

collaborators

7 papers

eess.IV2022

Automatic Quantification of Volumes and Biventricular Function in Cardiac Resonance. Validation of a New Artificial Intelligence Approach

Ariel H. Curiale, MatÍas E. Calandrelli, Lucca Dellazoppa +4

Background: Artificial intelligence techniques have shown great potential in cardiology, especially in quantifying cardiac biventricular function, volume, mass, and ejection fracti…

eess.IV2022★ 1 cited

Detection of Fibrosis in Cine Magnetic Resonance Images Using Artificial Intelligence Techniques

Ariel. H. Curiale, Facundo Cabrera, Pablo Jimenez +3

Background: Artificial intelligence techniques have demonstrated great potential in cardiology, especially to detect imperceptible patterns for the human eye. In this sense, these…

cs.CV2018★ 39 cited

Automatic quantification of the LV function and mass: a deep learning approach for cardiovascular MRI

Ariel H. Curiale, Flavio D. Colavecchia, German Mato

Objective: This paper proposes a novel approach for automatic left ventricle (LV) quantification using convolutional neural networks (CNN). Methods: The general framework consists…

cs.CV2017

Automatic Myocardial Segmentation by Using A Deep Learning Network in Cardiac MRI

Ariel H. Curiale, Flavio D. Colavecchia, Pablo Kaluza +2

Cardiac function is of paramount importance for both prognosis and treatment of different pathologies such as mitral regurgitation, ischemia, dyssynchrony and myocarditis. Cardiac…

nlin.AO2017★ 9 cited

Dynamical and Topological Aspects of Consensus Formation in Complex Networks

Andrés Chacoma, Germán Mato, Marcelo N. Kuperman

The present work analyses a particular scenario of consensus formation, where the individuals navigate across an underlying network defining the topology of the walks. The consensu…

q-bio.NC2013★ 2 cited

Linking dynamical and functional properties of intrinsically bursting neurons

Inés Samengo, Germán Mato, Daniel H. Elijah +2

Several studies have shown that bursting neurons can encode information in the number of spikes per burst: As the stimulus varies, so does the length of individual bursts. The repr…