Optical Coherence Tomography-Based Deep-Learning Models for Classifying Normal and Age-Related Macular Degeneration and Exudative and Non-Exudative Age-Related Macular Degeneration Changes
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M. Akiba | Naohiro Motozawa | Guangzhou An | S. Takagi | Shohei Kitahata | M. Mandai | Y. Hirami | H. Yokota | A. Tsujikawa | Masayo Takahashi | Y. Kurimoto | N. Motozawa
[1] P T de Jong,et al. An international classification and grading system for age-related maculopathy and age-related macular degeneration , 1995 .
[2] R. Klein,et al. The five-year incidence and progression of age-related maculopathy: the Beaver Dam Eye Study. , 1997, Ophthalmology.
[4] Jennifer I. Lim,et al. A randomized, placebo-controlled, clinical trial of high-dose supplementation with vitamins C and E, beta carotene, and zinc for age-related macular degeneration and vision loss: AREDS report no. 8. , 2001, Archives of ophthalmology.
[5] R. Klein,et al. Ten-year incidence and progression of age-related maculopathy: The Beaver Dam eye study. , 2002, Ophthalmology.
[6] Sanjay Sharma,et al. Progression of visual loss and time between initial assessment and treatment of wet age-related macular degeneration. , 2005, Canadian Journal of Ophthalmology-journal Canadien D Ophtalmologie.
[7] A. Ramé. [Age-related macular degeneration]. , 2006, Revue de l'infirmiere.
[8] Susan Schneider,et al. Ranibizumab versus verteporfin for neovascular age-related macular degeneration. , 2006, The New England journal of medicine.
[9] Paul Mitchell,et al. Ten-year incidence and progression of age-related maculopathy: the blue Mountains Eye Study. , 2007, Ophthalmology.
[10] William J Feuer,et al. An optical coherence tomography-guided, variable dosing regimen with intravitreal ranibizumab (Lucentis) for neovascular age-related macular degeneration. , 2007, American journal of ophthalmology.
[11] L. Akduman,et al. Management of Neovascular Age-related Macular Degeneration , 2008 .
[12] L. da Cruz,et al. Bevacizumab for neovascular age related macular degeneration (ABC Trial): multicentre randomised double masked study , 2010, BMJ : British Medical Journal.
[13] Yoshua Bengio,et al. Deep Sparse Rectifier Neural Networks , 2011, AISTATS.
[14] J. Duker,et al. The role of spectral-domain OCT in the diagnosis and management of neovascular age-related macular degeneration. , 2011, Ophthalmic surgery, lasers & imaging : the official journal of the International Society for Imaging in the Eye.
[15] Christian Simader,et al. Intravitreal aflibercept (VEGF trap-eye) in wet age-related macular degeneration. , 2012, Ophthalmology.
[16] I. Bhutto,et al. Understanding age-related macular degeneration (AMD): relationships between the photoreceptor/retinal pigment epithelium/Bruch's membrane/choriocapillaris complex. , 2012, Molecular aspects of medicine.
[17] Johanna M Seddon,et al. Age-related macular degeneration , 2012, The Lancet.
[18] M. Mandai,et al. Comparison of the effect of ranibizumab and verteporfin for polypoidal choroidal vasculopathy: 12-month LAPTOP study results. , 2013, American journal of ophthalmology.
[19] Kang Zhang,et al. Seven-year outcomes in ranibizumab-treated patients in ANCHOR, MARINA, and HORIZON: a multicenter cohort study (SEVEN-UP). , 2013, Ophthalmology.
[20] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[21] Carsten Framme,et al. TREAT-AND-EXTEND REGIMENS WITH ANTI-VEGF AGENTS IN RETINAL DISEASES: A Literature Review and Consensus Recommendations , 2015, Retina.
[22] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[23] Bolei Zhou,et al. Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Bianca S. Gerendas,et al. Correlation of 3-Dimensionally Quantified Intraretinal and Subretinal Fluid With Visual Acuity in Neovascular Age-Related Macular Degeneration. , 2016, JAMA ophthalmology.
[25] Malciolu Radu Alexandru,et al. Wet age related macular degeneration management and follow-up , 2016, Romanian journal of ophthalmology.
[26] A. Loewenstein,et al. Automated Identification of Lesion Activity in Neovascular Age-Related Macular Degeneration. , 2016, Ophthalmology.
[27] Aaron Y. Lee,et al. Reply. , 2016, Ophthalmology. Retina.
[28] Management of Neovascular Age-related Macular Degeneration: A Review on Landmark Randomized Controlled Trials , 2016, Middle East African journal of ophthalmology.
[29] Haipeng Shen,et al. Artificial intelligence in healthcare: past, present and future , 2017, Stroke and Vascular Neurology.
[30] Aaron Y. Lee,et al. Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration , 2016, bioRxiv.
[31] Philippe Burlina,et al. Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis , 2017, Comput. Biol. Medicine.
[32] Jiangtao Cui,et al. Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network , 2017, PloS one.
[33] Aaron Y. Lee,et al. Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration , 2016, bioRxiv.
[34] Neil J. Joshi,et al. Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks , 2017, JAMA ophthalmology.
[35] M. Treder,et al. Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning , 2018, Graefe's Archive for Clinical and Experimental Ophthalmology.
[36] Mark J J P van Grinsven,et al. Automated Staging of Age-Related Macular Degeneration Using Optical Coherence Tomography. , 2017, Investigative ophthalmology & visual science.
[37] Bianca S. Gerendas,et al. Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning. , 2017, Ophthalmology.
[38] Siamak Yousefi,et al. Detection of Longitudinal Visual Field Progression in Glaucoma Using Machine Learning. , 2018, American journal of ophthalmology.
[39] Daniel S. Kermany,et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning , 2018, Cell.
[40] Amir Sadeghipour,et al. Artificial intelligence in retina , 2018, Progress in Retinal and Eye Research.
[41] Geraint Rees,et al. Clinically applicable deep learning for diagnosis and referral in retinal disease , 2018, Nature Medicine.
[42] Hyewon Chung,et al. Automated Segmentation of Lesions Including Subretinal Hyperreflective Material in Neovascular Age-related Macular Degeneration. , 2018, American journal of ophthalmology.