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Non-intrusive inspection systems based on X-ray radiography techniques are routinely used at transport hubs to ensure the conformity of cargo content with the supplied shipping manifest. As trade volumes increase and regulations become more stringent, manual inspection by trained operators is less and less viable due to low throughput. Machine vision techniques can assist operators in their task by automating parts of the inspection workflow. Since cars are routinely involved in trafficking, export fraud, and tax evasion schemes, they represent an attractive target for automated detection and flagging for subsequent inspection by operators. 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. By introducing an oversampling scheme that suitably addresses the low number of car images available for training, we achieved 100% car image classification rate for a false positive rate of 1-in-454. Cars that were partially or completely obscured by other goods, a modus operandi frequently adopted by criminals, were correctly detected. We believe that this level of performance suggests that the method is suitable for deployment in the field. It is expected that the generic object detection workflow described can be extended to other object classes given the availability of suitable training data.

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. The full configuration interaction quantum Monte Carlo (FCIQMC) method exact thermal density matrix, respectively. In this article we describe the HANDE-QMC code, an open-source implementation of FCIQMC, CCMC and DMQMC, including initiator and semi-stochastic adaptations. We describe our code and demonstrate its use on three example systems; a molecule (nitric oxide), a model solid (the uniform electron gas), and a real solid (diamond). An illustrative tutorial is also included.

Precis: Pegasus outperformed 5 of the 6 ophthalmologists in terms of diagnostic performance, and there was no statistically significant difference between the deep learning system and the “best case” consensus between the ophthalmologists. The agreement between Pegasus and gold standard was 0.715, whereas the highest ophthalmologist agreement with the gold standard was 0.613. Furthermore, the high sensitivity of Pegasus makes it a valuable tool for screening patients with glaucomatous optic neuropathy. Purpose: The purpose of this study was to evaluate the performance of a deep learning system for the identification of glaucomatous optic neuropathy. Materials and Methods: Six ophthalmologists and the deep learning system, Pegasus, graded 110 color fundus photographs in this retrospective single-center study. Patient images were randomly sampled from the Singapore Malay Eye Study. Ophthalmologists and Pegasus were compared with each other and to the original clinical diagnosis given by the Singapore Malay Eye Study, which was defined as the gold standard. Pegasus’ performance was compared with the “best case” consensus scenario, which was the combination of ophthalmologists whose consensus opinion most closely matched the gold standard. The performance of the ophthalmologists and Pegasus, at the binary classification of nonglaucoma versus glaucoma from fundus photographs, was assessed in terms of sensitivity, specificity and the area under the receiver operating characteristic curve (AUROC), and the intraobserver and interobserver agreements were determined. Results: Pegasus achieved an AUROC of 92.6% compared with ophthalmologist AUROCs that ranged from 69.6% to 84.9% and the “best case” consensus scenario AUROC of 89.1%. Pegasus had a sensitivity of 83.7% and a specificity of 88.2%, whereas the ophthalmologists’ sensitivity ranged from 61.3% to 81.6% and specificity ranged from 80.0% to 94.1%. The agreement between Pegasus and gold standard was 0.715, whereas the highest ophthalmologist agreement with the gold standard was 0.613. Intraobserver agreement ranged from 0.62 to 0.97 for ophthalmologists and was perfect (1.00) for Pegasus. The deep learning system took ∼10% of the time of the ophthalmologists in determining classification. Conclusions: Pegasus outperformed 5 of the 6 ophthalmologists in terms of diagnostic performance, and there was no statistically significant difference between the deep learning system and the “best case” consensus between the ophthalmologists. The high sensitivity of Pegasus makes it a valuable tool for screening patients with glaucomatous optic neuropathy. Future work will extend this study to a larger sample of patients.

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. Methods:A retrospective study evaluating the diagnostic performance of an AI software (Pegasus v1.0, Visulytix Ltd., London UK) and comparing it to that of 243 European ophthalmologists and 208 British optometrists, as determined in previous studies, for the detection of glaucomatous optic neuropathy from 94 scanned stereoscopic photographic slides scanned into digital format. Results:Pegasus was able to detect glaucomatous optic neuropathy with an accuracy of 83.4% (95% CI: 77.5-89.2). This is comparable to an average ophthalmologist accuracy of 80.5% (95% CI: 67.2-93.8) and average optometrist accuracy of 80% (95% CI: 67-88) on the same images. In addition, the AI system had an intra-observer agreement (Cohen's Kappa, κ) of 0.74 (95% CI: 0.63-0.85), compared to 0.70 (range: -0.13-1.00; 95% CI: 0.67-0.73) and 0.71 (range: 0.08-1.00) for ophthalmologists and optometrists, respectively. There was no statistically significant difference between the performance of the deep learning system and ophthalmologists or optometrists. There was no statistically significant difference between the performance of the deep learning system and ophthalmologists or optometrists.Evaluation of an AI system for glaucoma detection Conclusion: T he AI syste m obtained a diagnostic performance and repeatability comparable to that of the ophthalmologists and optometrists. We conclude that deep learning based AI systems, such as Pegasus, demonstrate significant promise in the assisted detection of glaucomatous optic neuropathy.