1 Quellec G, "Optimal wavelet transform for the detection of microaneurysms in retina photographs" 27 (27): 1230-1241, 2008
2 Arunkumar R, "Multi-retinal disease classification by reduced deep learning features" 28 (28): 329-334, 2017
3 Choi JY, "Multi-categorical deep learning neural network to classify retinal images: a pilot study employing small database" 12 (12): e0187336-, 2017
4 Abràmoff MD, "Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning" 57 (57): 5200-5206, 2016
5 Krause J, "Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy" 125 (125): 1264-1272, 2018
6 Decencière E, "Feedback on a publicly distributed image database: the Messidor database" 33 (33): 231-234, 2014
7 Ting DS, "Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes" 318 (318): 2211-2223, 2017
8 Gulshan V, "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs" 316 (316): 2402-2410, 2016
9 Quellec G, "Deep image mining for diabetic retinopathy screening" 39 : 178-193, 2017
10 박성호, "Connecting Technological Innovation in Artificial Intelligence to Real-world Medical Practice through Rigorous Clinical Validation: What Peer-reviewed Medical Journals Could Do" 대한의학회 33 (33): 1-7, 2018
1 Quellec G, "Optimal wavelet transform for the detection of microaneurysms in retina photographs" 27 (27): 1230-1241, 2008
2 Arunkumar R, "Multi-retinal disease classification by reduced deep learning features" 28 (28): 329-334, 2017
3 Choi JY, "Multi-categorical deep learning neural network to classify retinal images: a pilot study employing small database" 12 (12): e0187336-, 2017
4 Abràmoff MD, "Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning" 57 (57): 5200-5206, 2016
5 Krause J, "Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy" 125 (125): 1264-1272, 2018
6 Decencière E, "Feedback on a publicly distributed image database: the Messidor database" 33 (33): 231-234, 2014
7 Ting DS, "Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes" 318 (318): 2211-2223, 2017
8 Gulshan V, "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs" 316 (316): 2402-2410, 2016
9 Quellec G, "Deep image mining for diabetic retinopathy screening" 39 : 178-193, 2017
10 박성호, "Connecting Technological Innovation in Artificial Intelligence to Real-world Medical Practice through Rigorous Clinical Validation: What Peer-reviewed Medical Journals Could Do" 대한의학회 33 (33): 1-7, 2018
11 Gargeya R, "Automated identification of diabetic retinopathy using deep learning" 124 (124): 7-962, 2017
12 Wong TY, "Artificial intelligence with deep learning technology looks into diabetic retinopathy screening" 316 (316): 2366-2367, 2016
13 Takahashi H, "Applying artificial intelligence to disease staging: deep learning for improved staging of diabetic retinopathy" 12 (12): e0179790-, 2017