FundusQ-Net: A regression quality assessment deep learning algorithm for fundus images quality grading

Pre-Training Without Natural Images

(2022) Hirokatsu Kataoka et al.   INTERNATIONAL JOURNAL OF COMPUTER VISION

Learning for retinal image quality assessment with label regularization

(2022) Tianjiao Guo et al.   COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

Glaucoma in Adults—Screening, Diagnosis, and Management

(2021) Joshua D. Stein et al.   JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION

The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

(2020) Davide Chicco et al.   BMC GENOMICS

Virtual Ophthalmology: Telemedicine in a Covid-19 Era

(2020) Sophia Mirza Saleem et al.   AMERICAN JOURNAL OF OPHTHALMOLOGY

CycleGAN-based deep learning technique for artifact reduction in fundus photography

(2020) Tae Keun Yoo et al.   GRAEFES ARCHIVE FOR CLINICAL AND EXPERIMENTAL OPHTHALMOLOGY

Automatic fundus image quality assessment on a continuous scale

(2020) Robert A. Karlsson et al.   COMPUTERS IN BIOLOGY AND MEDICINE

Quality and content analysis of fundus images using deep learning

(2019) Renoh Johnson Chalakkal et al.   COMPUTERS IN BIOLOGY AND MEDICINE

Fundus image quality assessment: survey, challenges, and future scope

(2019) Aditya Raj et al.   IET Image Processing

Accurate prediction of glaucoma from colour fundus images with a convolutional neural network that relies on active and transfer learning

(2019) Ruben Hemelings et al.   ACTA OPHTHALMOLOGICA

Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs

(2019) Hanruo Liu et al.   JAMA Ophthalmology

REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

(2019) José Ignacio Orlando et al.   MEDICAL IMAGE ANALYSIS

Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images

(2018) U Raghavendra et al.   INFORMATION SCIENCES

Automated Quality Assessment of Colour Fundus Images for Diabetic Retinopathy Screening in Telemedicine

(2018) Sajib Kumar Saha et al.   JOURNAL OF DIGITAL IMAGING

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

(2018) Zhixi Li et al.   OPHTHALMOLOGY

Automated Quality Assessment of Fundus Images via Analysis of Illumination, Naturalness and Structure

(2018) Feng Shao et al.   IEEE Access

Global causes of blindness and distance vision impairment 1990–2020: a systematic review and meta-analysis

(2017) Seth R Flaxman et al.   Lancet Global Health

ImageNet Large Scale Visual Recognition Challenge

(2015) Olga Russakovsky et al.   INTERNATIONAL JOURNAL OF COMPUTER VISION

Identification of suitable fundus images using automated quality assessment methods

(2014) Ugur Sevik et al.   JOURNAL OF BIOMEDICAL OPTICS

The number of ophthalmologists in practice and training worldwide: a growing gap despite more than 200 000 practitioners

(2012) Serge Resnikoff et al.   BRITISH JOURNAL OF OPHTHALMOLOGY

Automatic detection of age-related macular degeneration pathologies in retinal fundus images

(2012) Ayşegül Güven   COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING