Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs

Opportunities and obstacles for deep learning in biology and medicine

(2018) Travers Ching et al.   Journal of the Royal Society Interface

Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy

(2018) Jonathan Krause et al.   OPHTHALMOLOGY

Missing data hinder replication of artificial intelligence studies

(2018) Matthew Hutson   SCIENCE

Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes

(2017) Daniel Shu Wei Ting et al.   JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION

A survey on deep learning in medical image analysis

(2017) Geert Litjens et al.   MEDICAL IMAGE ANALYSIS

Automated Identification of Diabetic Retinopathy Using Deep Learning

(2017) Rishab Gargeya et al.   OPHTHALMOLOGY

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

(2016) Varun Gulshan et al.   JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION

Reality check on reproducibility

(2016)   NATURE

ImageNet Large Scale Visual Recognition Challenge

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

FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE

(2014) Etienne Decencière et al.   Image Analysis & Stereology

Announcement: Reducing our irreproducibility

(2013)   NATURE

Optimal Wavelet Transform for the Detection of Microaneurysms in Retina Photographs

(2008) G. Quellec et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING