Related references
Note: Only part of the references are listed.Transfer Learning for Automated OCTA Detection of Diabetic Retinopathy
David Le et al.
TRANSLATIONAL VISION SCIENCE & TECHNOLOGY (2020)
A machine learning approach for retinal images analysis as an objective screening method for children with autism spectrum disorder
Maria Lai et al.
ECLINICALMEDICINE (2020)
Retinal blood vessel extraction employing effective image features and combination of supervised and unsupervised machine learning methods
Mahdi Hashemzadeh et al.
ARTIFICIAL INTELLIGENCE IN MEDICINE (2019)
Automated Diabetic Retinopathy Detection Based on Binocular Siamese-Like Convolutional Neural Network
Xianglong Zeng et al.
IEEE ACCESS (2019)
Microaneurysm detection using fully convolutional neural networks
Piotr Chudzik et al.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2018)
Age-related Macular Degeneration detection using deep convolutional neural network
Jen Hong Tan et al.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2018)
Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy
Jonathan Krause et al.
OPHTHALMOLOGY (2018)
Algorithms for red lesion detection in Diabetic Retinopathy: A review
R. S. Biyani et al.
BIOMEDICINE & PHARMACOTHERAPY (2018)
Computer-aided diagnosis of glaucoma using fundus images: A review
Yuki Hagiwara et al.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2018)
Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning
Marc Aubreville et al.
SCIENTIFIC REPORTS (2017)