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Found 6 papers, 1 papers with code
AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge
Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible.
Disease classification of macular Optical Coherence Tomography scans using deep learning software: validation on independent, multi-centre data
Pegasus-OCT was shown to be able to detect AMD, DME and general anomalies in OCT volumes acquired across multiple independent sites with high performance.
Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment Study
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.
Automated detection of smuggled high-risk security threats using Deep Learning
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.
Automated X-ray Image Analysis for Cargo Security: Critical Review and Future Promise
We review the relatively immature field of automated image analysis for X-ray cargo imagery.
Detection of concealed cars in complex cargo X-ray imagery using Deep Learning
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.
