439 citations · 634 across the 13 of their papers we have counts for
8 papers · 1 filter
To Recurse or not to Recurse,a Low Dose CT Study
Shabab Bazrafkan, Vincent Van Nieuwenhove, Jan Sijbers
Restoring high-quality CT images from low dose CT counterparts is an ill-posed, nonlinear problem to which Deep Learning approaches have been giving superior solutions compared to…
Deep Neural Network Assisted Iterative Reconstruction Method for Low Dose CT
Shabab Bazrafkan, Vincent Van Nieuwenhove, Joris Soons +2
Low Dose Computed Tomography suffers from a high amount of noise and/or undersampling artefacts in the reconstructed image. In the current article, a Deep Learning technique is exp…
Deep Learning Based Computed Tomography Whys and Wherefores
Shabab Bazrafkan, Vincent Van Nieuwenhove, Joris Soons +2
This is an article about the Computed Tomography (CT) and how Deep Learning influences CT reconstruction pipeline, especially in low dose scenarios.
Versatile Auxiliary Classifier with Generative Adversarial Network (VAC+GAN)
Shabab Bazrafkan, Hossein Javidnia, Peter Corcoran
One of the most interesting challenges in Artificial Intelligence is to train conditional generators which are able to provide labeled adversarial samples drawn from a specific dis…
Versatile Auxiliary Regressor with Generative Adversarial network (VAR+GAN)
Shabab Bazrafkan, Peter Corcoran
Being able to generate constrained samples is one of the most appealing applications of the deep generators. Conditional generators are one of the successful implementations of suc…
Face Synthesis with Landmark Points from Generative Adversarial Networks and Inverse Latent Space Mapping
Shabab Bazrafkan, Hossein Javidnia, Peter Corcoran
Facial landmarks refer to the localization of fundamental facial points on face images. There have been a tremendous amount of attempts to detect these points from facial images ho…