General deep learning model for detecting diabetic retinopathy

[1] Abràmoff MD(2018)Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices NPJ Digital Med 1 39-1561

[2] Lavin PT(2019)Deep learning in ophthalmology: the technical and clinical considerations Prog Retin Eye Res 72 100759-313

[3] Birch M(2019)Artificial intelligence screening for diabetic retinopathy: the real-world emerging application Curr Diab Rep 19 72-596

[4] Shah N(2018)Application of artificial intelligence in ophthalmology Int J Ophthalmol 11 1555-57

[5] Folk JC(2018)Deep learning in ophthalmology: a review Can J Ophthalmol 53 39-905

[6] Ting DSW(2018)Retinal lesion detection with deep learning using image patches Invest Ophthalmol Vis Sci 59 590-357

[7] Peng L(2018)Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms BMC Bioinform 19 283-232

[8] Varadarajan AV(2018)Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy Biomed Eng Lett 8 41-15

[9] Bellemo V(2018)SMOTE for learning from imbalanced data: progress and challenges, Marking the 15-year Anniversary J Artif Intell Res 61 863-20

[10] Lim G(2002)SMOTE: synthetic minority over-sampling technique J Artif Intell Res 16 321-35

[1] Abràmoff MD(2018)Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices NPJ Digital Med 1 39-1561

[2] Lavin PT(2019)Deep learning in ophthalmology: the technical and clinical considerations Prog Retin Eye Res 72 100759-313

[3] Birch M(2019)Artificial intelligence screening for diabetic retinopathy: the real-world emerging application Curr Diab Rep 19 72-596

[4] Shah N(2018)Application of artificial intelligence in ophthalmology Int J Ophthalmol 11 1555-57

[5] Folk JC(2018)Deep learning in ophthalmology: a review Can J Ophthalmol 53 39-905

[6] Ting DSW(2018)Retinal lesion detection with deep learning using image patches Invest Ophthalmol Vis Sci 59 590-357

[7] Peng L(2018)Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms BMC Bioinform 19 283-232

[8] Varadarajan AV(2018)Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy Biomed Eng Lett 8 41-15

[9] Bellemo V(2018)SMOTE for learning from imbalanced data: progress and challenges, Marking the 15-year Anniversary J Artif Intell Res 61 863-20

[10] Lim G(2002)SMOTE: synthetic minority over-sampling technique J Artif Intell Res 16 321-35