Gianluca Pontone
Orcid: 0000-0002-1339-6679
According to our database1,
Gianluca Pontone
authored at least 14 papers
between 2018 and 2024.
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Bibliography
2024
Two new calibration techniques of lumped-parameter mathematical models for the cardiovascular system.
CoRR, 2024
CAD-RADS scoring of coronary CT angiography with Multi-Axis Vision Transformer: A clinically-inspired deep learning pipeline.
Comput. Methods Programs Biomed., 2024
Aortic valve neocuspidization and bioprosthetic valves: Evaluating turbulence haemodynamics.
Comput. Biol. Medicine, 2024
2023
A token-mixer architecture for CAD-RADS classification of coronary stenosis on multiplanar reconstruction CT images.
Comput. Biol. Medicine, February, 2023
2022
Proceedings of the Medical Imaging 2022: Image Processing, 2022
2021
A computational model applied to myocardial perfusion in the human heart: From large coronaries to microvasculature.
J. Comput. Phys., 2021
Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks.
CoRR, 2021
Automated left and right ventricular chamber segmentation in cardiac magnetic resonance images using dense fully convolutional neural network.
Comput. Methods Programs Biomed., 2021
2020
3D right ventricular endocardium segmentation in cardiac magnetic resonance images by using a new inter-modality statistical shape modelling method.
Biomed. Signal Process. Control., 2020
Automated Left and Right Chamber Segmentation in Cardiac MRI Using Dense Fully Convolutional Neural Network.
Proceedings of the Computing in Cardiology, 2020
A Novel Approach Based on Spatio-temporal Features and Random Forest for Scar Detection Using Cine Cardiac Magnetic Resonance Images.
Proceedings of the Computing in Cardiology, 2020
2018
A statistical shape model of the left ventricle from real-time 3D echocardiography and its application to myocardial segmentation of cardiac magnetic resonance images.
Comput. Biol. Medicine, 2018
Automated Scar Segmentation From Cardiac Magnetic Resonance-Late Gadolinium Enhancement Images Using a Deep-Learning Approach.
Proceedings of the Computing in Cardiology, 2018