TY - GEN
T1 - Large-Scale Left and Right Eye Classification in Retinal Images
AU - Liu, Peng
AU - Gu, Zaiwang
AU - Liu, Fan
AU - Jiang, Yuming
AU - Jiang, Shanshan
AU - Mao, Haoyu
AU - Cheng, Jun
AU - Duan, Lixin
AU - Liu, Jiang
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - Left and right eye information is an important priori for automatic retinal fundus image analysis. However, such information is often not available or even wrongly provided in many datasets. In this work, we spend a considerable amount of efforts in manually annotating the left and right eyes from the large-scale Kaggle Diabetic Retinopathy dataset consisting of 88,702 fundus images, based on our developed online labeling system. With the newly annotated large-scale dataset, we also train classification models based on convolutional neural networks to discriminate left and right eyes in fundus images. As experimentally evaluated on the Kaggle and Origa dataset, our trained deep learning models achieve 99.90% and 99.23% in term of classification accuracy, respectively, which can be considered for practical use.
AB - Left and right eye information is an important priori for automatic retinal fundus image analysis. However, such information is often not available or even wrongly provided in many datasets. In this work, we spend a considerable amount of efforts in manually annotating the left and right eyes from the large-scale Kaggle Diabetic Retinopathy dataset consisting of 88,702 fundus images, based on our developed online labeling system. With the newly annotated large-scale dataset, we also train classification models based on convolutional neural networks to discriminate left and right eyes in fundus images. As experimentally evaluated on the Kaggle and Origa dataset, our trained deep learning models achieve 99.90% and 99.23% in term of classification accuracy, respectively, which can be considered for practical use.
KW - Convolutional neural networks
KW - Left and right eye classification
UR - http://www.scopus.com/inward/record.url?scp=85053887032&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00949-6_31
DO - 10.1007/978-3-030-00949-6_31
M3 - Conference contribution
AN - SCOPUS:85053887032
SN - 9783030009489
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 261
EP - 268
BT - Computational Pathology and Ophthalmic Medical Image Analysis - First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Proceedings
A2 - Taylor, Zeike
A2 - Bogunovic, Hrvoje
A2 - Snead, David
A2 - Garvin, Mona K.
A2 - Chen, Xin Jan
A2 - Ciompi, Francesco
A2 - Xu, Yanwu
A2 - Maier-Hein, Lena
A2 - Veta, Mitko
A2 - Trucco, Emanuele
A2 - Stoyanov, Danail
A2 - Rajpoot, Nasir
A2 - van der Laak, Jeroen
A2 - Martel, Anne
A2 - McKenna, Stephen
PB - Springer Verlag
T2 - 1st International Workshop on Computational Pathology, COMPAY 2018 and 5th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2018 Held in Conjunction with MICCAI 2018
Y2 - 16 September 2018 through 20 September 2018
ER -
