Related references
Note: Only part of the references are listed.A deep learning interpretable classifier for diabetic retinopathy disease grading
Jordi de la Torre et al.
NEUROCOMPUTING (2020)
Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration
Cristina Gonzalez-Gonzalo et al.
ACTA OPHTHALMOLOGICA (2020)
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Ramprasaath R. Selvaraju et al.
INTERNATIONAL JOURNAL OF COMPUTER VISION (2020)
Neural Style Transfer: A Review
Yongcheng Jing et al.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS (2020)
Diagnostic Accuracy of a Device for the Automated Detection of Diabetic Retinopathy in a Primary Care Setting
Frank D. Verbraak et al.
DIABETES CARE (2019)
Accuracy of ultrawide-field fundus ophthalmoscopy-assisted deep learning for detecting treatment-naive proliferative diabetic retinopathy
Toshihiko Nagasawa et al.
INTERNATIONAL OPHTHALMOLOGY (2019)
Deep learning in ophthalmology: The technical and clinical considerations
Daniel S. W. Ting et al.
PROGRESS IN RETINAL AND EYE RESEARCH (2019)
Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network
Yi-Peng Liu et al.
ARTIFICIAL INTELLIGENCE IN MEDICINE (2019)
Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading
Jaakko Sahlsten et al.
SCIENTIFIC REPORTS (2019)
Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India
Varun Gulshan et al.
JAMA OPHTHALMOLOGY (2019)
An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis
Po-Hsuan Cameron Chen et al.
NATURE MEDICINE (2019)
Diagnostic Accuracy of Community-Based Diabetic Retinopathy Screening With an Offline Artificial Intelligence System on a Smartphone
Sundaram Natarajan et al.
JAMA OPHTHALMOLOGY (2019)
Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening
Tao Li et al.
INFORMATION SCIENCES (2019)
Artificial intelligence and deep learning in ophthalmology
Daniel Shu Wei Ting et al.
BRITISH JOURNAL OF OPHTHALMOLOGY (2019)
Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy
Rajiv Raman et al.
EYE (2019)
A guide to deep learning in healthcare
Andre Esteva et al.
NATURE MEDICINE (2019)
Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy
Rory Sayres et al.
OPHTHALMOLOGY (2019)
Visualizing Deep Learning Models for the Detection of Referable Diabetic Retinopathy and Glaucoma
Stuart Keel et al.
JAMA OPHTHALMOLOGY (2019)
Comparison of Early Treatment Diabetic Retinopathy Study Standard 7-Field Imaging With Ultrawide-Field Imaging for Determining Severity of Diabetic Retinopathy
Lloyd Paul Aiello et al.
JAMA OPHTHALMOLOGY (2019)
Intermediate Goals in Deep Learning for Retinal Image Analysis
Gilbert Lim et al.
COMPUTER VISION - ACCV 2018 WORKSHOPS (2019)
Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study
Valentina Bellemo et al.
LANCET DIGITAL HEALTH (2019)
Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program
Paisan Raumviboonsuk et al.
NPJ DIGITAL MEDICINE (2019)
Deep learning in estimating prevalence and systemic risk factors for diabetic retinopathy: a multi-ethnic study
Daniel S. W. Ting et al.
NPJ DIGITAL MEDICINE (2019)
Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence
Ramachandran Rajalakshmi et al.
EYE (2018)
A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography
Felix Grassmann et al.
OPHTHALMOLOGY (2018)
Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs
Zhixi Li et al.
OPHTHALMOLOGY (2018)
Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy
Jonathan Krause et al.
OPHTHALMOLOGY (2018)
Clinical Applicability of Deep Learning System in Detecting Tuberculosis with Chest Radiography
Daniel S. W. Ting et al.
RADIOLOGY (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)
Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks
James M. Brown et al.
JAMA OPHTHALMOLOGY (2018)
A Weakly-Supervised Framework for Interpretable Diabetic Retinopathy Detection on Retinal Images
Pedro Costa et al.
IEEE ACCESS (2018)
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
Ryan Poplin et al.
NATURE BIOMEDICAL ENGINEERING (2018)
Artificial intelligence powers digital medicine
Alexander L. Fogel et al.
NPJ DIGITAL MEDICINE (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)
Automated detection of diabetic retinopathy lesions on ultrawidefield pseudocolour images
Kang Wang et al.
ACTA OPHTHALMOLOGICA (2018)
Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System
Amber A. van der Heijden et al.
ACTA OPHTHALMOLOGICA (2018)
Advances in Retinal Imaging and Applications in Diabetic Retinopathy Screening: A Review
Beau J. Fenner et al.
OPHTHALMOLOGY AND THERAPY (2018)
Evaluation of Artificial Intelligence-Based Grading of Diabetic Retinopathy in Primary Care
Yogesan Kanagasingam et al.
JAMA NETWORK OPEN (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)
Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
Babak Ehteshami Bejnordi 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)
Deep image mining for diabetic retinopathy screening
Gwenole Quellec et al.
MEDICAL IMAGE ANALYSIS (2017)
Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image
Kele Xu et al.
MOLECULES (2017)
Machine Learning Has Arrived!
Aaron Lee et al.
OPHTHALMOLOGY (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 at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks
Paras Lakhani et al.
RADIOLOGY (2017)
Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks
Philippe M. Burlina et al.
JAMA OPHTHALMOLOGY (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)
A tool for automated diabetic retinopathy pre-screening based on retinal image computer analysis
Manuel E. Gegundez-Arias et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2017)
Automatic detection of diabetic retinopathy features in Ultra-Wide Field retinal images
Anastasia Levenkova et al.
MEDICAL IMAGING 2017: COMPUTER-AIDED DIAGNOSIS (2017)
Deep learning approach for diabetic retinopathy screening
E. Colas et al.
ACTA OPHTHALMOLOGICA (2016)
Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review
Daniel Shu Wei Ting et al.
CLINICAL AND EXPERIMENTAL OPHTHALMOLOGY (2016)
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Nima Tajbakhsh et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2016)
Artificial Intelligence With Deep Learning Technology Looks Into Diabetic Retinopathy Screening
Tien Yin Wong et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2016)
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)
Cost-effectiveness of a National Telemedicine Diabetic Retinopathy Screening Program in Singapore
Hai V. Nguyen et al.
OPHTHALMOLOGY (2016)
Ultra-wide-field imaging in diabetic retinopathy; an overview
Khalil Ghasemi Falavarjani et al.
JOURNAL OF CURRENT OPHTHALMOLOGY (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)
A study of whether automated Diabetic Retinopathy Image Assessment could replace manual grading steps in the English National Screening Programme
Venediktos V. Kapetanakis et al.
JOURNAL OF MEDICAL SCREENING (2015)
Peripheral Lesions Identified on Ultrawide Field Imaging Predict Increased Risk of Diabetic Retinopathy Progression over 4 Years
Paolo S. Silva et al.
OPHTHALMOLOGY (2015)
Validation of Smartphone Based Retinal Photography for Diabetic Retinopathy Screening
Ramachandran Rajalakshmi et al.
PLOS ONE (2015)
Validating Retinal Fundus Image Analysis Algorithms: Issues and a Proposal
Emanuele Trucco et al.
INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE (2013)
Screening for Diabetic Retinopathy and Diabetic Macular Edema in the United Kingdom
Tunde Peto et al.
CURRENT DIABETES REPORTS (2012)
Global Prevalence and Major Risk Factors of Diabetic Retinopathy
Joanne W. Y. Yau et al.
DIABETES CARE (2012)
Costs and consequences of automated algorithms versus manual grading for the detection of referable diabetic retinopathy
G. S. Scotland et al.
BRITISH JOURNAL OF OPHTHALMOLOGY (2010)
A Survey on Transfer Learning
Sinno Jialin Pan et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2010)
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)
Cost-effectiveness of implementing automated grading within the national screening programme for diabetic retinopathy in Scotland
G. S. Scotland et al.
BRITISH JOURNAL OF OPHTHALMOLOGY (2007)
Automatic detection of red lesions in digital color fundus photographs
M Niemeijer et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2005)
Ridge-based vessel segmentation in color images of the retina
J Staal et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2004)
Effect of mydriasis and different field strategies on digital image screening of diabetic eye disease
H Murgatroyd et al.
BRITISH JOURNAL OF OPHTHALMOLOGY (2004)
Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening
D Usher et al.
DIABETIC MEDICINE (2004)
Automated detection of diabetic retinopathy on digital fundus images
C Sinthanayothin et al.
DIABETIC MEDICINE (2002)