{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T17:00:11Z","timestamp":1789837211808,"version":"4.0.1"},"reference-count":33,"publisher":"Association for Research in Vision and Ophthalmology (ARVO)","issue":"6","license":[{"start":{"date-parts":[[2018,12,28]],"date-time":"2018-12-28T00:00:00Z","timestamp":1545955200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Trans. Vis. Sci. Tech."],"published-print":{"date-parts":[[2018,12,28]]},"DOI":"10.1167\/tvst.7.6.41","type":"journal-article","created":{"date-parts":[[2018,12,28]],"date-time":"2018-12-28T18:30:24Z","timestamp":1546021824000},"page":"41","source":"Crossref","is-referenced-by-count":159,"title":["Deep Learning-Based Automated Classification of Multi-Categorical Abnormalities From Optical Coherence Tomography Images"],"prefix":"10.1167","volume":"7","author":[{"given":"Wei","family":"Lu","sequence":"first","affiliation":[{"name":"Eye Center, Renmin Hospital of Wuhan University, Eye Institute of Wuhan University, Wuhan, Hubei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Tong","sequence":"additional","affiliation":[{"name":"Eye Center, Renmin Hospital of Wuhan University, Eye Institute of Wuhan University, Wuhan, Hubei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Yu","sequence":"additional","affiliation":[{"name":"Wuhan Haixingtong Technology Limited Company, Wuhan, Hubei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiqiao","family":"Xing","sequence":"additional","affiliation":[{"name":"Eye Center, Renmin Hospital of Wuhan University, Eye Institute of Wuhan University, Wuhan, Hubei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changzheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Eye Center, Renmin Hospital of Wuhan University, Eye Institute of Wuhan University, Wuhan, Hubei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yin","family":"Shen","sequence":"additional","affiliation":[{"name":"Eye Center, Renmin Hospital of Wuhan University, Eye Institute of Wuhan University, Wuhan, Hubei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"27","reference":[{"key":"i2164-2591-7-6-41-b01","doi-asserted-by":"crossref","unstructured":"LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521: 436\u2013444.","DOI":"10.1038\/nature14539"},{"key":"i2164-2591-7-6-41-b02","doi-asserted-by":"crossref","unstructured":"Banino A, Barry C, Uria B, et al. Vector-based navigation using grid-like representations in artificial agents. Nature. 2018; 557: 429\u2013433.","DOI":"10.1038\/s41586-018-0102-6"},{"key":"i2164-2591-7-6-41-b03","doi-asserted-by":"crossref","unstructured":"Bejnordi BE, Zuidhof G, Balkenhol M, et al. Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images. J Med Imaging. 2017; 4: 044504.","DOI":"10.1117\/1.JMI.4.4.044504"},{"key":"i2164-2591-7-6-41-b04","doi-asserted-by":"crossref","unstructured":"Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017; 542: 115\u2013118.","DOI":"10.1038\/nature21056"},{"key":"i2164-2591-7-6-41-b05","doi-asserted-by":"crossref","unstructured":"Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One. 2017; 12: e0174944.","DOI":"10.1371\/journal.pone.0174944"},{"key":"i2164-2591-7-6-41-b06","doi-asserted-by":"crossref","unstructured":"van Ginneken B. Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning. Radiol Physics Tech. 2017; 10: 23\u201332.","DOI":"10.1007\/s12194-017-0394-5"},{"key":"i2164-2591-7-6-41-b07","doi-asserted-by":"crossref","unstructured":"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: 2402.","DOI":"10.1001\/jama.2016.17216"},{"key":"i2164-2591-7-6-41-b08","doi-asserted-by":"crossref","unstructured":"Rahimy E. Deep learning applications in ophthalmology. Curr Opin Ophthalmol. 2018: 1.","DOI":"10.1097\/ICU.0000000000000470"},{"key":"i2164-2591-7-6-41-b09","doi-asserted-by":"crossref","unstructured":"Abr\u00e0moff MD, Lou Y, Erginay A, et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthamol Vis Sci. 2016; 57: 5200.","DOI":"10.1167\/iovs.16-19964"},{"key":"i2164-2591-7-6-41-b10","doi-asserted-by":"crossref","unstructured":"Adhi M, Duker JS. Optical coherence tomography\u2013current and future applications. Curr Opin Ophthalmol. 2013; 24: 213\u2013221.","DOI":"10.1097\/ICU.0b013e32835f8bf8"},{"key":"i2164-2591-7-6-41-b11","doi-asserted-by":"crossref","unstructured":"Gabriele ML, Wollstein G, Ishikawa H, et al. Optical coherence tomography: history, current status, and laboratory work. Invest Ophthamol Vis Sci. 2011; 52: 2425\u20132436.","DOI":"10.1167\/iovs.10-6312"},{"key":"i2164-2591-7-6-41-b12","doi-asserted-by":"crossref","unstructured":"Chen J, Lee L. Clinical applications and new developments of optical coherence tomography: an evidence-based review. Clin Exp Optom. 2007; 90: 317\u2013335.","DOI":"10.1111\/j.1444-0938.2007.00151.x"},{"key":"i2164-2591-7-6-41-b13","doi-asserted-by":"crossref","unstructured":"ElTanboly A, Ismail M, Shalaby A, et al. A computer-aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images. Med Phys. 2017; 44: 914\u2013923.","DOI":"10.1002\/mp.12071"},{"key":"i2164-2591-7-6-41-b14","doi-asserted-by":"crossref","unstructured":"Kermany DS, Goldbaum M, Cai W, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018; 172: 1122\u20131131.e9.","DOI":"10.1016\/j.cell.2018.02.010"},{"key":"i2164-2591-7-6-41-b15","doi-asserted-by":"crossref","unstructured":"Schlegl T, Waldstein SM, Bogunovic H, et al. Fully automated detection and quantification of macular fluid in OCT using deep learning. Ophthalmology. 2018; 125: 549\u2013558.","DOI":"10.1016\/j.ophtha.2017.10.031"},{"key":"i2164-2591-7-6-41-b16","doi-asserted-by":"crossref","unstructured":"Lee CS, Tyring AJ, Deruyter NP, Wu Y, Rokem A, Lee AY. Deep-learning based, automated segmentation of macular edema in optical coherence tomography. Biomed Opt Express. 2017; 8: 3440.","DOI":"10.1364\/BOE.8.003440"},{"key":"i2164-2591-7-6-41-b17","unstructured":"Cherkassky V, Mulier F. Statistical learning theory. Encyclopedia Sci Learning. 1998; 41: 3185."},{"key":"i2164-2591-7-6-41-b18","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA; 2016: 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"i2164-2591-7-6-41-b19","unstructured":"Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA; 2015: 1\u20139."},{"key":"i2164-2591-7-6-41-b20","doi-asserted-by":"crossref","unstructured":"He K, Sun J. Convolutional neural networks at constrained time cost. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA; 2015: 5353\u20135360.","DOI":"10.1109\/CVPR.2015.7299173"},{"key":"i2164-2591-7-6-41-b21","doi-asserted-by":"crossref","unstructured":"Burlina PM, Joshi N, Pekala M, Pacheco KD, Freund DE, Bressler NM. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. Jama Ophthalmol. 2017; 135: 1170.","DOI":"10.1001\/jamaophthalmol.2017.3782"},{"key":"i2164-2591-7-6-41-b22","doi-asserted-by":"crossref","unstructured":"Russakovsky O, Deng J, Su H, et al. ImageNet large scale visual recognition challenge. Int J Comput Vis. 2015; 115: 211\u2013252.","DOI":"10.1007\/s11263-015-0816-y"},{"key":"i2164-2591-7-6-41-b23","doi-asserted-by":"crossref","unstructured":"Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977; 33: 159\u2013174.","DOI":"10.2307\/2529310"},{"key":"i2164-2591-7-6-41-b24","doi-asserted-by":"crossref","unstructured":"Li Z, He Y, Keel S, Meng W, Chang RT, He M. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology. 2018; 125: 1207\u20131208.","DOI":"10.1016\/j.ophtha.2018.01.023"},{"key":"i2164-2591-7-6-41-b25","doi-asserted-by":"crossref","unstructured":"Burlina P, Pacheco KD, Joshi N, Freund DE, Bressler NM. Comparing humans and deep learning performance for grading AMD: a study in using universal deep features and transfer learning for automated AMD analysis. Comput Biol Med. 2017; 82: 80\u201386.","DOI":"10.1016\/j.compbiomed.2017.01.018"},{"key":"i2164-2591-7-6-41-b26","doi-asserted-by":"crossref","unstructured":"Abbas Q, Fondon I, Sarmiento A, Jim\u00e9nez S, Alemany P. Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features. Med Biol Eng Comput. 2017; 55: 1959\u20131974.","DOI":"10.1007\/s11517-017-1638-6"},{"key":"i2164-2591-7-6-41-b27","doi-asserted-by":"crossref","unstructured":"Choi JY, Yoo TK, Seo JG, Kwak J, Um TT, Rim TH. Multi-categorical deep learning neural network to classify retinal images: a pilot study employing small database. PLoS One. 2017; 12: e0187336.","DOI":"10.1371\/journal.pone.0187336"},{"key":"i2164-2591-7-6-41-b28","doi-asserted-by":"crossref","unstructured":"Roy AG, Conjeti S, Karri SPK, et al. ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed Opt Exp. 2017; 8: 3627.","DOI":"10.1364\/BOE.8.003627"},{"key":"i2164-2591-7-6-41-b29","doi-asserted-by":"crossref","unstructured":"Muhammad H, Fuchs TJ, De CN, et al. Hybrid deep learning on single wide-field optical coherence tomography scans accurately classifies glaucoma suspects. J Glaucoma. 2017: 1.","DOI":"10.1097\/IJG.0000000000000765"},{"key":"i2164-2591-7-6-41-b30","doi-asserted-by":"crossref","unstructured":"Xu X, Lee K, Zhang L, Sonka M, Abramoff MD. Stratified sampling voxel classification for segmentation of intraretinal and subretinal fluid in longitudinal clinical OCT data. IEEE Trans Med Imaging. 2015; 34: 1616\u20131623.","DOI":"10.1109\/TMI.2015.2408632"},{"key":"i2164-2591-7-6-41-b31","doi-asserted-by":"crossref","unstructured":"Fang L, Cunefare D, Wang C, Guymer RH, Li S, Farsiu S. Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomed Opt Exp. 2017; 8: 2732.","DOI":"10.1364\/BOE.8.002732"},{"key":"i2164-2591-7-6-41-b32","doi-asserted-by":"crossref","unstructured":"Gargeya R, Leng T. Automated identification of diabetic retinopathy using deep learning. Ophthalmology. 2017; 124: 962\u2013969.","DOI":"10.1016\/j.ophtha.2017.02.008"},{"key":"i2164-2591-7-6-41-b33","doi-asserted-by":"crossref","unstructured":"Quellec G, Charri\u00e8re K, Boudi Y, Cochener B, Lamard M. Deep image mining for diabetic retinopathy screening. Med Image Anal. 2017; 39: 178.","DOI":"10.1016\/j.media.2017.04.012"}],"container-title":["Translational Vision Science &amp; Technology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/tvst.arvojournals.org\/article.aspx?articleid=2719997","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,11,13]],"date-time":"2019-11-13T01:49:00Z","timestamp":1573609740000},"score":1,"resource":{"primary":{"URL":"http:\/\/tvst.arvojournals.org\/article.aspx?doi=10.1167\/tvst.7.6.41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,28]]},"references-count":33,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2018,11,1]]}},"URL":"https:\/\/doi.org\/10.1167\/tvst.7.6.41","relation":{},"ISSN":["2164-2591"],"issn-type":[{"value":"2164-2591","type":"print"}],"subject":[],"published":{"date-parts":[[2018,12,28]]}}}