The SUSTech-SYSU dataset for automated exudate detection and diabetic retinopathy grading

Scientific Data
Li LinXiaoying Tang

Abstract

Automated detection of exudates from fundus images plays an important role in diabetic retinopathy (DR) screening and evaluation, for which supervised or semi-supervised learning methods are typically preferred. However, a potential limitation of supervised and semi-supervised learning based detection algorithms is that they depend substantially on the sample size of training data and the quality of annotations, which is the fundamental motivation of this work. In this study, we construct a dataset containing 1219 fundus images (from DR patients and healthy controls) with annotations of exudate lesions. In addition to exudate annotations, we also provide four additional labels for each image: left-versus-right eye label, DR grade (severity scale) from three different grading protocols, the bounding box of the optic disc (OD), and fovea location. This dataset provides a great opportunity to analyze the accuracy and reliability of different exudate detection, OD detection, fovea localization, and DR classification algorithms. Moreover, it will facilitate the development of such algorithms in the realm of supervised and semi-supervised learning.

References

Aug 1, 1966·Psychological Reports·J J Bartko
Jun 30, 2000·IEEE Transactions on Medical Imaging·A HooverM Goldbaum
Sep 18, 2003·Ophthalmology·C P WilkinsonUNKNOWN Global Diabetic Retinopathy Project Group
Feb 10, 2004·IEEE Transactions on Bio-medical Engineering·Huiqi Li, Opas Chutatape
Apr 16, 2004·IEEE Transactions on Medical Imaging·Joes StaalBram van Ginneken
Oct 22, 2008·Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society·Akara SopharakThomas H Williamson
Jun 23, 2009·Medical Image Analysis·Clara I SánchezRoberto Hornero
Feb 4, 2012·Diabetes Care·Joanne W Y YauUNKNOWN Meta-Analysis for Eye Disease (META-EYE) Study Group
Nov 13, 2012·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Shih-Chia HuangYi-Sheng Chiu
Feb 15, 2014·Investigative Ophthalmology & Visual Science·Jan LammerUrsula Schmidt-Erfurth
May 4, 2015·Computers in Biology and Medicine·Désiré SidibéFabrice Mériaudeau
Apr 10, 2016·Biomedizinische Technik. Biomedical Engineering·D SanthiS Karkuzhali
Jan 28, 2017·Scientific Reports·Michiru NishitaYasuhiro Minami
Jul 15, 2018·The Lancet. Diabetes & Endocrinology·Charumathi SabanayagamTien Y Wong
Nov 24, 2019·Ophthalmology·Christina J FlaxelGui-Shuang Ying

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Software Mentioned

aiforeye
Net
Labelimg
originalImages
odFoveaLabels
BBR
exudateLabels
IDRiD
STARE

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