no code implementations • 4 Jun 2019 • Thomas W. Rogers, Nicolas Jaccard, Francis Carbonaro, Hans G. Lemij, Koenraad A. Vermeer, Nicolaas J. Reus, Sameer Trikha
Objectives: To evaluate the performance of a deep learning based Artificial Intelligence (AI) software for detection of glaucoma from stereoscopic optic disc photographs, and to compare this performance to the performance of a large cohort of ophthalmologists and optometrists.
1 code implementation • 28 Nov 2018 • James S. Spencer, Nick S. Blunt, Seonghoon Choi, Jiri Etrych, Maria-Andreea Filip, W. M. C. Foulkes, Ruth S. T. Franklin, Will J. Handley, Fionn D. Malone, Verena A. Neufeld, Roberto Di Remigio, Thomas W. Rogers, Charles J. C. Scott, James J. Shepherd, William A. Vigor, Joseph Weston, RuQing Xu, Alex J. W. Thom
Building on the success of Quantum Monte Carlo techniques such as diffusion Monte Carlo, alternative stochastic approaches to solve electronic structure problems have emerged over the last decade.
Computational Physics
no code implementations • 9 Sep 2016 • Nicolas Jaccard, Thomas W. Rogers, Edward J. Morton, Lewis D. Griffin
In this contribution, we demonstrate for the first time the use of Convolutional Neural Networks (CNNs), a type of Deep Learning, to automate the detection of SMTs in fullsize X-ray images of cargo containers.
no code implementations • 2 Aug 2016 • Thomas W. Rogers, Nicolas Jaccard, Edward J. Morton, Lewis D. Griffin
We review the relatively immature field of automated image analysis for X-ray cargo imagery.
no code implementations • 26 Jun 2016 • Nicolas Jaccard, Thomas W. Rogers, Edward J. Morton, Lewis D. Griffin
In this contribution, we describe a method for the detection of cars in X-ray cargo images based on trained-from-scratch Convolutional Neural Networks.