https://orcid.org/0000-0003-0462-119X
References
- Krizhevsky A, Sutskever I, Hinton: G. ImageNet classification with deep convolutional neural networks. NPIS. 2012;1:1097–1105.
- LeCun Y, Kavukcuoglu K, Farabet C. Convolutional networks and applications in vision. Proceedings of 2010 IEEE International Symposium on Circuits and Systems; 2010; Paris: 253–256.
- Russakovsky O, Deng J, Su H, et al. ImageNet large scale visual recognition challenge. Int J Comput Vis. 2015;115(3):211–252. doi:10.1007/s11263-015-0816-y
- AI system is better than human doctors at predicting breast cancer. Available from: https://www.newscientist.com/article/2228752-ai-system-is-better-than-human-doctors-at-predicting-breast-cancer/#ixzz6Tm62cEbz. Accessed February 12, 2021.
- Halevy A, Norvig P, Pereira F. The unreasonable effectiveness of data. IEEE Intell Syst. 2009;24(2):8–12. doi:10.1109/MIS.2009.36
- Labelbox. Available from: https://labelbox.com/. Accessed February 12, 2021
- Supervise.ly. Available from: https://supervise.ly/. Accessed February 12, 2021.
- LabelImg. Available from: https://github.com/tzutalin/labelImg. Accessed February 12, 2021.
- Everything you need to know to successfully develop your data annotation processes. Available from: https://medium.com/thelaunchpad/spinning-up-an-annotation-team-c74c6765531b. Accessed February 12, 2021.
- Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–2410. doi:10.1001/jama.2016.17216
- Wei Q, Li X. Learn to segment retinal lesions and beyond. arXiv: 1912.11619v1 [cs.CV]. 2019.
- Haloi M. Improved microaneurysm detection using deep neural networks. ArXiv: 1505.04424 [Cs]. arXiv.org. July 2016. Available from: http://arxiv.org/abs/1505.04424.
- Lam C, Yu C, Huang L, et al. Retinal lesion detection with deep learning using image patches. Invest Ophthalmol Vis Sci. 2018;59(1):590–596. doi:10.1167/iovs.17-22721
- Park SJ, Shin JY, Kim S, et al. Image reading tool for efficient generation of a multi-dimensional categorical image database for machine learning algorithm training. J Korean Med Sci. 2018;33(43):e239. doi:10.3346/jkms.2018.33.e239
- Rasta SH, Partovi ME, Seyedarabi H, Javadzadeh A. A comparative study on preprocessing techniques in diabetic retinopathy retinal images: illumination correction and contrast enhancement. J Med Signals Sens. 2015;5(1):40–48. doi:10.4103/2228-7477.150414
- Abràmoff MD, Lou Y, Erginay A, Clarida W, Ryan Amelon JC. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci. 2016;57(13):5200–5206. doi:10.1167/iovs.16-19964
- Krause J, Gulshan V, Rahimy E, et al. Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy. Ophthalmology. 2018;125(8):1264–1272. doi:10.1016/j.ophtha.2018.01.034
- Schaekermann M, Hammel N, Terry M, et al. Remote tool-based adjudication for grading diabetic retinopathy. Trans Vis Sci Tech. 2019;8(6):40. doi:10.1167/tvst.8.6.40
- Bababekova Y, Rosenfield M, Hue JE, Huang RR. Font size and viewing distance of handheld smart phones. Optom Vis Sci. 2011;88(7):795–797. doi:10.1097/OPX.0b013e3182198792
- A new image format for the Web. Available from: https://developers.google.com/speed/webp. Accessed February 12, 2021.
- Kaggle diabetic retinopathy detection competition. Available from: https://www.kaggle.com/c/diabetic-retinopathy-detection. Accessed February 12, 2021.
- Decencière E, Etienne D, Xiwei Z, et al. Feedback on a publicly distributed image database: the messidor database. Image Anal Stereol. 2014;33(3):231–234. doi:10.5566/ias.1155
- APTOS. Blindness detection; 2019. Available from: https://www.kaggle.com/c/aptos2019-blindness-detection/. Accessed February 12, 2021.
- Peking university international competition on ocular disease intelligent recognition. Available from: https://odir2019.grand-challenge.org/dataset/. Accessed February 12, 2021.
- Porwal P, Pachade S, Kamble R, ManeshKokare G, Deskmukh VS. Indian diabetic retinopathy image dataset (IDRiD). IEEE Dataport. 2018. doi:10.21227/H25W98
- Sivaswamy J, Krishnadas SR, Chakravarty A, Joshi GD, Ujjwal SA. Comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis. JSM Biomed Imaging Data Papers. 2015; 2(1):1004.
- American Academy of Ophthalmology. International clinical diabetic retinopathy disease severity scale, detailed table. Available from: http://www.icoph.org/dynamic/attachments/resources/diabetic-retinopathy-detail.pdf. Accessed February 12, 2021.
- Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018;28(1):39. doi:10.1038/s41746-018-0040-6