Search Results for author:
Found 7 papers, 2 papers with code
Classification of Influenza Hemagglutinin Protein Sequences using Convolutional Neural Networks
This paper focuses on accurately predicting if an Influenza type A virus can infect specific hosts, and more specifically, Human, Avian and Swine hosts, using only the protein sequence of the HA gene.
Sinogram Denoise Based on Generative Adversarial Networks
Finally, to demonstrate the efficacy and capability of the proposed method results from real-world DAT-SPECT sinograms are presented.
Deep Convolutional Neural Network for Low Projection SPECT Imaging Reconstruction
In this paper, we present a novel method for tomographic image reconstruction in SPECT imaging with a low number of projections.
SPECT Angle Interpolation Based on Deep Learning Methodologies
For evaluation of the efficacy of the method, phantoms based on Shepp Logan, with various noise levels added were used, and the resulting interpolated sinograms are reconstructed using Ordered Subset Expectation Maximization (OSEM) and compared to the reconstructions of the original sinograms.
Prediction of Influenza A virus infections in humans using an Artificial Neural Network learning approach
In this paper, to accurately predict if an Influenza type A virus has the capability to infect human hosts, by using only the HA gene, is therefore developed and tested.
SPECT Imaging Reconstruction Method Based on Deep Convolutional Neural Network
In this paper, we explore a novel method for tomographic image reconstruction in the field of SPECT imaging.
Unsupervised identification of the phase transition on the 2D-Ising model
We study numerically the relation between one latent dimension extracted from the autoencoder to the critical temperature $T_c$.
Statistical Mechanics Disordered Systems and Neural Networks High Energy Physics - Lattice Computational Physics
