Related references
Note: Only part of the references are listed.Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Daniel S. Kermany et al.
CELL (2018)
Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence
Ramachandran Rajalakshmi et al.
EYE (2018)
Clinical Report Guided Retinal Microaneurysm Detection With Multi-Sieving Deep Learning
Ling Dai et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2018)
Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy
Jonathan Krause et al.
OPHTHALMOLOGY (2018)
Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning
Thomas Schlegl et al.
OPHTHALMOLOGY (2018)
Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study
Stuart Keel et al.
SCIENTIFIC REPORTS (2018)
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
Ryan Poplin et al.
NATURE BIOMEDICAL ENGINEERING (2018)
Classification of Diabetic Retinopathy Images by Using Deep Learning Models
Suvajit Dutta et al.
INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING (2018)
Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices
Michael D. Abramoff et al.
NPJ DIGITAL MEDICINE (2018)
Retinal Lesion Detection With Deep Learning Using Image Patches
Carson Lam et al.
INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE (2018)
Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes
Daniel Shu Wei Ting et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2017)
Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features
Qaisar Abbas et al.
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING (2017)
Automated Diabetic Retinopathy Image Assessment Software Diagnostic Accuracy and Cost-Effectiveness Compared with Human Graders
Adnan Tufail Frcophth et al.
OPHTHALMOLOGY (2017)
Automated Identification of Diabetic Retinopathy Using Deep Learning
Rishab Gargeya et al.
OPHTHALMOLOGY (2017)
Deep-learning based, automated segmentation of macular edema in optical coherence tomography
Cecilia S. Lee et al.
BIOMEDICAL OPTICS EXPRESS (2017)
Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy
Hidenori Takahashi et al.
PLOS ONE (2017)
Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database
Joon Yul Choi et al.
PLOS ONE (2017)
Detection of Diabetic Retinopathy Based on a Convolutional Neural Network Using Retinal Fundus Images
Gabriel Garcia et al.
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, PT II (2017)
Classifying environmental sounds using image recognition networks
Venkatesh Boddapati et al.
KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS (2017)
The English National Screening Programme for diabetic retinopathy 2003-2016
Peter H. Scanlon
ACTA DIABETOLOGICA (2017)
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
Varun Gulshan et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2016)
Applicability of Smartphone-Based Screening Programs
Sunir J. Garg
JAMA OPHTHALMOLOGY (2016)
Evaluation of Automated Teleretinal Screening Program for Diabetic Retinopathy
O. Bennett Walton et al.
JAMA OPHTHALMOLOGY (2016)
Suitability of a Low-Cost, Handheld, Nonmydriatic Retinograph for Diabetic Retinopathy Diagnosis
Gwenole Quellec et al.
TRANSLATIONAL VISION SCIENCE & TECHNOLOGY (2016)
Ultra-wide-field imaging in diabetic retinopathy; an overview
Khalil Ghasemi Falavarjani et al.
JOURNAL OF CURRENT OPHTHALMOLOGY (2016)
SparkGIS: Efficient Comparison and Evaluation of Algorithm Results in Tissue Image Analysis Studies
Furqan Baig et al.
BIOMEDICAL DATA MANAGEMENT AND GRAPH ONLINE QUERYING (2016)
Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning
Michael David Abramoff et al.
INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE (2016)
The Prototype of Computer-Assisted for Screening and Identifying Severity of Diabetic Retinopathy Automatically from Color Fundus Images for mHealth System in Thailand
Weeagul Pratumgul et al.
2016 INTERNATIONAL ELECTRICAL ENGINEERING CONGRESS, IEECON2016 (2016)
Comparison of Smartphone Ophthalmoscopy With Slit-Lamp Biomicroscopy for Grading Diabetic Retinopathy
Andrea Russo et al.
AMERICAN JOURNAL OF OPHTHALMOLOGY (2015)
Deep learning in neural networks: An overview
Juergen Schmidhuber
NEURAL NETWORKS (2015)
Machine learning applications in cancer prognosis and prediction
Konstantina Kourou et al.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL (2015)
Validation of Smartphone Based Retinal Photography for Diabetic Retinopathy Screening
Ramachandran Rajalakshmi et al.
PLOS ONE (2015)
A Web-Based Platform for Automated Diabetic Retinopathy Screening
Jose Tomas Arenas-Cavalli et al.
KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 19TH ANNUAL CONFERENCE, KES-2015 (2015)
Three-Dimensional Spectral-Domain Optical Coherence Tomography Data Analysis for Glaucoma Detection
Juan Xu et al.
PLOS ONE (2013)
COMPARISON OF STANDARDIZED CLINICAL CLASSIFICATION WITH FUNDUS PHOTOGRAPH GRADING FOR THE ASSESSMENT OF DIABETIC RETINOPATHY AND DIABETIC MACULAR EDEMA SEVERITY
Sapna Gangaputra et al.
RETINA-THE JOURNAL OF RETINAL AND VITREOUS DISEASES (2013)
Automated Analysis of Retinal Images for Detection of Referable Diabetic Retinopathy
Michael D. Abramoff et al.
JAMA OPHTHALMOLOGY (2013)
Digital versus Film Fundus Photography for Research Grading of Diabetic Retinopathy Severity
Helen K. Li et al.
INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE (2010)
A masking device and method to create custom laser exposure patterns for verteporfin photodynamic therapy
Edwin H. Ryan et al.
RETINA-THE JOURNAL OF RETINAL AND VITREOUS DISEASES (2008)
Evaluation of a System for Automatic Detection of Diabetic Retinopathy From Color Fundus Photographs in a Large Population of Patients With Diabetes
Michael D. Abramoff et al.
DIABETES CARE (2008)
Agreement between clinician and reading center gradings of diabetic retinopathy severity level at baseline in a phase 2 study of intravitreal bevacizumab for diabetic macular edema
Ingrid U. Scott et al.
RETINA-THE JOURNAL OF RETINAL AND VITREOUS DISEASES (2008)
Interobserver agreement in the interpretation of single-field digital fundus images for diabetic retinopathy screening
P Ruamviboonsuk et al.
OPHTHALMOLOGY (2006)
Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales
CP Wilkinson et al.
OPHTHALMOLOGY (2003)
Progress towards automated diabetic ocular screening: a review of image analysis and intelligent systems for diabetic retinopathy
T Teng et al.
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING (2002)
Screening for diabetic retinopathy using computer based image analysis and statistical classification
BM Ege et al.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2000)
Share Paper
