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Volumn 1, Issue 4, 2017, Pages 322-327

Deep Learning Is Effective for Classifying Normal versus Age-Related Macular Degeneration OCT Images

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EID: 85027881492     PISSN: None     EISSN: 24686530     Source Type: Journal    
DOI: 10.1016/j.oret.2016.12.009     Document Type: Article
Times cited : (511)

References (24)
  • 1
    • 85027876514 scopus 로고    scopus 로고
    • Medicare Provider Utilization and Payment Data: Physician and Other Supplier. Accessed May 20
    • Centers for Medicare & Medicaid Services. Medicare Provider Utilization and Payment Data: Physician and Other Supplier. https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/Physician-and-Other-Supplier.html. Accessed May 20, 2016.
    • (2016)
    • Centers for Medicare & Medicaid Services1
  • 2
    • 0026254046 scopus 로고
    • Optical coherence tomography
    • Huang, D., Swanson, E.A., Lin, C.P., et al. Optical coherence tomography. Science 254 (1991), 1178–1181.
    • (1991) Science , vol.254 , pp. 1178-1181
    • Huang, D.1    Swanson, E.A.2    Lin, C.P.3
  • 3
    • 84875260404 scopus 로고    scopus 로고
    • Diffusion of technologies for the care of older adults with exudative age-related macular degeneration
    • Stein, J.D., Hanrahan, B.W., Comer, G.M., Sloan, F.A., Diffusion of technologies for the care of older adults with exudative age-related macular degeneration. Am J Ophthalmol 155 (2013), 688–696.e2.
    • (2013) Am J Ophthalmol , vol.155 , pp. 688-696.e2
    • Stein, J.D.1    Hanrahan, B.W.2    Comer, G.M.3    Sloan, F.A.4
  • 6
    • 67349108048 scopus 로고    scopus 로고
    • Comparison of spectral-domain versus time-domain optical coherence tomography in management of age-related macular degeneration with ranibizumab
    • Sayanagi, K., Sharma, S., Yamamoto, T., Kaiser, P.K., Comparison of spectral-domain versus time-domain optical coherence tomography in management of age-related macular degeneration with ranibizumab. Ophthalmology 116 (2009), 947–955.
    • (2009) Ophthalmology , vol.116 , pp. 947-955
    • Sayanagi, K.1    Sharma, S.2    Yamamoto, T.3    Kaiser, P.K.4
  • 7
    • 67649998896 scopus 로고    scopus 로고
    • Evaluation of optical coherence tomography retinal thickness parameters for use in clinical trials for neovascular age-related macular degeneration
    • Keane, P.A., Liakopoulos, S., Jivrajka, R.V., et al. Evaluation of optical coherence tomography retinal thickness parameters for use in clinical trials for neovascular age-related macular degeneration. Invest Opthalmol Vis Sci 50 (2009), 3378–3385.
    • (2009) Invest Opthalmol Vis Sci , vol.50 , pp. 3378-3385
    • Keane, P.A.1    Liakopoulos, S.2    Jivrajka, R.V.3
  • 8
    • 84908297394 scopus 로고    scopus 로고
    • Visual acuity and central retinal thickness: fulfilment of retreatment criteria for recurrent neovascular AMD in routine clinical care
    • Reznicek, L., Muhr, J., Ulbig, M., et al. Visual acuity and central retinal thickness: fulfilment of retreatment criteria for recurrent neovascular AMD in routine clinical care. Br J Ophthalmol 98 (2014), 1333–1337.
    • (2014) Br J Ophthalmol , vol.98 , pp. 1333-1337
    • Reznicek, L.1    Muhr, J.2    Ulbig, M.3
  • 9
    • 84906282829 scopus 로고    scopus 로고
    • Optical coherence tomography monitoring strategies for A-VEGF–treated age-related macular degeneration: an evidence-based analysis
    • [online]
    • Pron, G., Optical coherence tomography monitoring strategies for A-VEGF–treated age-related macular degeneration: an evidence-based analysis. Ont Health Technol Assess Ser 14:10 (2014), 1–64 [online] http://www.hqontario.ca/evidence/publications-and-ohtac-recommendations/ontario-health-technology-assessment-series/OCT-monitoring-strategies.
    • (2014) Ont Health Technol Assess Ser , vol.14 , Issue.10 , pp. 1-64
    • Pron, G.1
  • 10
    • 84927599465 scopus 로고    scopus 로고
    • Geographic variation in radiologist capacity and widespread implementation of lung cancer CT screening
    • Smieliauskas, F., MacMahon, H., Salgia, R., Shih, Y.-C.T., Geographic variation in radiologist capacity and widespread implementation of lung cancer CT screening. J Med Screen 21 (2014), 207–215.
    • (2014) J Med Screen , vol.21 , pp. 207-215
    • Smieliauskas, F.1    MacMahon, H.2    Salgia, R.3    Shih, Y.-C.T.4
  • 11
    • 81555205692 scopus 로고    scopus 로고
    • Computer-aided diagnosis: how to move from the laboratory to the clinic
    • Van Ginneken, B., Schaefer-Prokop, C.M., Prokop, M., Computer-aided diagnosis: how to move from the laboratory to the clinic. Radiology 261 (2011), 719–732.
    • (2011) Radiology , vol.261 , pp. 719-732
    • Van Ginneken, B.1    Schaefer-Prokop, C.M.2    Prokop, M.3
  • 12
    • 84986274457 scopus 로고    scopus 로고
    • Classification of SD-OCT volumes using local binary patterns: experimental validation for DME detection
    • Lemaître, G., Rastgoo, M., Massich, J., et al. Classification of SD-OCT volumes using local binary patterns: experimental validation for DME detection. J Ophthalmol 2016 (2016), 1–14.
    • (2016) J Ophthalmol , vol.2016 , pp. 1-14
    • Lemaître, G.1    Rastgoo, M.2    Massich, J.3
  • 13
    • 84942367248 scopus 로고    scopus 로고
    • Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images
    • Srinivasan, P.P., Kim, L.A., Mettu, P.S., et al. Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomed Opt Express 5 (2014), 3568–3577.
    • (2014) Biomed Opt Express , vol.5 , pp. 3568-3577
    • Srinivasan, P.P.1    Kim, L.A.2    Mettu, P.S.3
  • 14
    • 80052138068 scopus 로고    scopus 로고
    • Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding
    • Liu, Y.-Y., Chen, M., Ishikawa, H., Wollstein, G., Schuman, J.S., Rehg, J.M., Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding. Med Image Anal 15 (2011), 748–759.
    • (2011) Med Image Anal , vol.15 , pp. 748-759
    • Liu, Y.-Y.1    Chen, M.2    Ishikawa, H.3    Wollstein, G.4    Schuman, J.S.5    Rehg, J.M.6
  • 15
    • 77958488310 scopus 로고    scopus 로고
    • Deep machine learning - a new frontier in artificial intelligence research [research frontier]
    • Arel, I., Rose, D.C., Karnowski, T.P., Deep machine learning - a new frontier in artificial intelligence research [research frontier]. IEEE Comput Intell M 5 (2010), 13–18.
    • (2010) IEEE Comput Intell M , vol.5 , pp. 13-18
    • Arel, I.1    Rose, D.C.2    Karnowski, T.P.3
  • 16
    • 84876231242 scopus 로고    scopus 로고
    • Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems; 2012:1097-1105. Accessed October 20
    • Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems; 2012:1097-1105. http://papers.nips.cc/paper/4824-imagenet-classification-w. Accessed October 20, 2016.
    • (2016)
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 17
    • 84906489074 scopus 로고    scopus 로고
    • Visualizing and understanding convolutional networks. In: European Conference on Computer Vision.2014:818–833. Accessed October 20
    • Zeiler MD, Fergus R. Visualizing and understanding convolutional networks. In: European Conference on Computer Vision.2014:818–833. http://link.springer.com/chapter/10.1007/978-3-319-10590-1_53. Accessed October 20, 2016.
    • (2016)
    • Zeiler, M.D.1    Fergus, R.2
  • 18
    • 85162511224 scopus 로고    scopus 로고
    • Learning convolutional feature hierarchies for visual recognition. In: Advances in Neural Information Processing Systems; 2010:1090-1098. Accessed October 20
    • Kavukcuoglu K, Sermanet P, Boureau Y-L, Gregor K, Mathieu M, Cun YL. Learning convolutional feature hierarchies for visual recognition. In: Advances in Neural Information Processing Systems; 2010:1090-1098. http://papers.nips.cc/paper/4133-learning-convolutional-feature-hierarchies-for-visual-recognition. Accessed October 20, 2016.
    • (2016)
    • Kavukcuoglu, K.1    Sermanet, P.2    Boureau, Y.-L.3    Gregor, K.4    Mathieu, M.5    Cun, Y.L.6
  • 19
    • 84982243237 scopus 로고    scopus 로고
    • Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier
    • Asaoka, R., Murata, H., Iwase, A., Araie, M., Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier. Ophthalmology 123 (2016), 1974–1980.
    • (2016) Ophthalmology , vol.123 , pp. 1974-1980
    • Asaoka, R.1    Murata, H.2    Iwase, A.3    Araie, M.4
  • 20
    • 84990193991 scopus 로고    scopus 로고
    • Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning
    • Abràmoff, M.D., Lou, Y., Erginay, A., et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci 57 (2016), 5200–5206.
    • (2016) Invest Ophthalmol Vis Sci , vol.57 , pp. 5200-5206
    • Abràmoff, M.D.1    Lou, Y.2    Erginay, A.3
  • 21
    • 84946714467 scopus 로고    scopus 로고
    • Automatic feature learning to grade nuclear cataracts based on deep learning
    • Gao, X., Lin, S., Wong, T.Y., Automatic feature learning to grade nuclear cataracts based on deep learning. IEEE Trans Biomed Eng 62 (2015), 2693–2701.
    • (2015) IEEE Trans Biomed Eng , vol.62 , pp. 2693-2701
    • Gao, X.1    Lin, S.2    Wong, T.Y.3
  • 22
    • 84978683018 scopus 로고    scopus 로고
    • Segmentation of the foveal microvasculature using deep learning networks
    • Prentašic, P., Heisler, M., Mammo, Z., et al. Segmentation of the foveal microvasculature using deep learning networks. J Biomed Opt, 21, 2016, 075008.
    • (2016) J Biomed Opt , vol.21 , pp. 075008
    • Prentašic, P.1    Heisler, M.2    Mammo, Z.3
  • 23
    • 85027842655 scopus 로고    scopus 로고
    • Very deep convolutional networks for large-scale image recognition. ArXiv:1409.1556. 2014. Accessed October 20
    • Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. ArXiv:1409.1556. 2014. http://arxiv.org/abs/1409.1556. Accessed October 20, 2016.
    • (2016)
    • Simonyan, K.1    Zisserman, A.2
  • 24
    • 84862277874 scopus 로고    scopus 로고
    • Understanding the difficulty of training deep feedforward neural networks
    • Accessed October 20, 2016
    • Glorot, X., Bengio, Y., Understanding the difficulty of training deep feedforward neural networks. Aistats, 9, 2010, 249–256 Accessed October 20, 2016 http://www.jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf?hc_location=ufi.
    • (2010) Aistats , vol.9 , pp. 249-256
    • Glorot, X.1    Bengio, Y.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.