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Journal ArticleDOI

Automated Identification of Diabetic Retinopathy Using Deep Learning

Rishab Gargeya, +1 more
- 01 Jul 2017 - 
- Vol. 124, Iss: 7, pp 962-969
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TLDR
A fully data-driven artificial intelligence-based grading algorithm can be used to screen fundus photographs obtained from diabetic patients and to identify, with high reliability, which cases should be referred to an ophthalmologist for further evaluation and treatment.
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This article is published in Ophthalmology.The article was published on 2017-07-01. It has received 864 citations till now. The article focuses on the topics: Receiver operating characteristic.

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Citations
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Journal ArticleDOI

Artificial intelligence and deep learning in ophthalmology

TL;DR: There are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI ‘black-box’ algorithms.
Journal ArticleDOI

Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs.

TL;DR: A deep learning system can detect referable GON with high sensitivity and specificity and coexistence of high or pathologic myopia is the most common cause resulting in false-negative results.
Journal ArticleDOI

Artificial intelligence in retina.

TL;DR: In this paper, a fully automated AI-based system has been proposed for screening of diabetic retinopathy (DR) in diabetic macular and retinal disease using a convolutional neural network.
Journal ArticleDOI

Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning

TL;DR: Deep learning in retinal image analysis achieves excellent accuracy for the differential detection of retinal fluid types across the most prevalent exudative macular diseases and OCT devices.
References
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TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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ImageNet Classification with Deep Convolutional Neural Networks

TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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Proceedings Article

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

TL;DR: Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.
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Greedy function approximation: A gradient boosting machine.

TL;DR: A general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion, and specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification.
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