47 citations · 76 across the 8 of their papers we have counts for
10 papers
Hamiltonian dynamics for stochastic reconstruction in emission tomography
T. Leontiou, A. Frixou, E. Ttofi +6
Objective: To develop a practical stochastic reconstruction framework for emission tomography that generates ensembles of data-compatible images and enables uncertainty quantificat…
Classification of Influenza Hemagglutinin Protein Sequences using Convolutional Neural Networks
Charalambos Chrysostomou, Floris Alexandrou, Mihalis A. Nicolaou +1
The Influenza virus can be considered as one of the most severe viruses that can infect multiple species with often fatal consequences to the hosts. The Hemagglutinin (HA) gene of…
Sinogram Denoise Based on Generative Adversarial Networks
Charalambos Chrysostomou
A novel method for sinogram denoise based on Generative Adversarial Networks (GANs) in the field of SPECT imaging is presented. Projection data from software phantoms were used to…
Deep Convolutional Neural Network for Low Projection SPECT Imaging Reconstruction
Charalambos Chrysostomou, Loizos Koutsantonis, Christos Lemesios +1
In this paper, we present a novel method for tomographic image reconstruction in SPECT imaging with a low number of projections. Deep convolutional neural networks (CNN) are employ…
SPECT Angle Interpolation Based on Deep Learning Methodologies
Charalambos Chrysostomou, Loizos Koutsantonis, Christos Lemesios +1
A novel method for SPECT angle interpolation based on deep learning methodologies is presented. Projection data from software phantoms were used to train the proposed model. For ev…
Prediction of Influenza A virus infections in humans using an Artificial Neural Network learning approach
Charalambos Chrysostomou, Harris Partaourides, Huseyin Seker
The Influenza type A virus can be considered as one of the most severe viruses that can infect multiple species with often fatal consequences to the hosts. The Haemagglutinin (HA)…
SPECT Imaging Reconstruction Method Based on Deep Convolutional Neural Network
Charalambos Chrysostomou, Loizos Koutsantonis, Christos Lemesios +1
In this paper, we explore a novel method for tomographic image reconstruction in the field of SPECT imaging. Deep Learning methodologies and more specifically deep convolutional ne…
Neural network-based modelling of unresolved stresses in a turbulent reacting flow with mean shear
Zacharias M. Nikolaou, Charalambos Chrysostomou, Yuki Minamoto +1
Data-driven methods for modelling purposes in fluid mechanics are a promising alternative given the continuous increase of both computational power and data-storage capabilities. H…
The critical temperature of the 2D-Ising model through Deep Learning Autoencoders
Constantia Alexandrou, Andreas Athenodorou, Charalambos Chrysostomou +1
We investigate deep learning autoencoders for the unsupervised recognition of phase transitions in physical systems formulated on a lattice. We focus our investigation on the 2-dim…
Modelling turbulent premixed flames using convolutional neural networks: application to sub-grid scale variance and filtered reaction rate
Zacharias M. Nikolaou, Charalambos Chrysostomou, Luc Vervisch +1
A purely data-driven approach using deep convolutional neural networks is discussed in the context of Large Eddy Simulation (LES) of turbulent premixed flames. The assessment of th…