{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T05:49:05Z","timestamp":1745473745496,"version":"3.37.3"},"publisher-location":"Cham","reference-count":46,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319659800"},{"type":"electronic","value":"9783319659817"}],"license":[{"start":{"date-parts":[[2017,11,14]],"date-time":"2017-11-14T00:00:00Z","timestamp":1510617600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-65981-7_10","type":"book-chapter","created":{"date-parts":[[2017,11,13]],"date-time":"2017-11-13T02:03:51Z","timestamp":1510538631000},"page":"263-293","source":"Crossref","is-referenced-by-count":8,"title":["Computer Aided Diagnosis in Ophthalmology: Deep Learning Applications"],"prefix":"10.1007","author":[{"given":"Jos\u00e9 N.","family":"Galveia","sequence":"first","affiliation":[]},{"given":"Ant\u00f3nio","family":"Travassos","sequence":"additional","affiliation":[]},{"given":"Francisca A.","family":"Quadros","sequence":"additional","affiliation":[]},{"given":"Lu\u00eds A.","family":"da Silva Cruz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,11,14]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Abr\u00e0moff MD, Folk JC, Han DP, Walker JD, Williams DF, Russell SR et al (2013) Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol 131:351\u2013357. http:\/\/dx.doi.org\/10.1001\/jamaophthalmol.2013.1743","DOI":"10.1001\/jamaophthalmol.2013.1743"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Abr\u00e0moff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, Niemeijer M (2016) Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci 57(13):5200\u20135206. http:\/\/dx.doi.org\/10.1167\/iovs.16-19964","DOI":"10.1167\/iovs.16-19964"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Age-Related Eye Disease Study Research Group (2001) The Age-Related Eye Disease Study system for classifying age-related macular degeneration from stereoscopic color fundus photographs: the Age-Related Eye Disease Study Report Number 6. Am J Ophthalmol 132(6):668\u2013681. https:\/\/doi.org\/S0002939401012181","DOI":"10.1016\/S0002-9394(01)01218-1"},{"key":"10_CR4","unstructured":"Benson WE, Blodi BA, Boldt HC, Murray TG, Regillo CD, Scott IU (2008) Age-related macular degeneration, preferred practice pattern. Am Acad Ophthalmol"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Bourne WM (2003) Biology of the corneal endothelium in health and disease. Eye (London, England) 17(8):912\u20138. http:\/\/dx.doi.org\/10.1038\/sj.eye.6700559","DOI":"10.1038\/sj.eye.6700559"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Burlina P, Pacheco KD, Joshi N, Freund DE, Bressler NM (2017) 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 82:80\u201386. http:\/\/dx.doi.org\/10.1016\/j.compbiomed.2017.01.018","DOI":"10.1016\/j.compbiomed.2017.01.018"},{"key":"10_CR7","unstructured":"Cheriguene S, Azizi N, Zemmal N, Dey N, Djellali H, Farah N (2015) Optimized tumor breast cancer classification using combining random subspace and static classifiers selection paradigms. In Hassanien A-E, Grosan C, Tolba MF (eds) Applications of intelligent optimization in biology and medicine\u2014intelligent systems reference library, vol 96. Springer, Berlin, pp 289\u2013307"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Chhablani J, Barteselli G, Wang H, El-Emam S, Kozak I, Doede AL et al (2012) Repeatability and reproducibility of manual choroidal volume measurements using enhanced depth imaging optical coherence tomography. Invest Ophthalmol Vis Sci 53(4):2274\u20132280. http:\/\/dx.doi.org\/10.1167\/iovs.12-9435","DOI":"10.1167\/iovs.12-9435"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Eadie LH, Taylor P, Gibson AP (2012) A systematic review of computer-assisted diagnosis in diagnostic cancer imaging. Eur J Radiol 81(1):e70\u2013e76. http:\/\/dx.doi.org\/10.1016\/j.ejrad.2011.01.098","DOI":"10.1016\/j.ejrad.2011.01.098"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR, Owen CG, Barman SA (2012) An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans Biomed Eng 59(9):2538\u20132548. http:\/\/dx.doi.org\/10.1109\/TBME.2012.2205687","DOI":"10.1109\/TBME.2012.2205687"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Gao X, Lin S, Wong TY (2015) Automatic feature learning to grade nuclear cataracts based on deep learning. IEEE Trans Biomed Eng 62(11):2693\u20132701. http:\/\/dx.doi.org\/10.1109\/TBME.2015.2444389","DOI":"10.1109\/TBME.2015.2444389"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Gargeya R, Leng T (2017) Automated identification of diabetic retinopathy using deep learning. Ophthalmology 1\u20138. http:\/\/dx.doi.org\/10.1016\/j.ophtha.2017.02.008","DOI":"10.1016\/j.ophtha.2017.02.008"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Giancardo L, Member S, Meriaudeau F, Karnowski TP, Li Y, Jr Tobin KW et al (2011) Microaneurysm detection with radon transform-based classification on retina images pp 5939\u20135942","DOI":"10.1109\/IEMBS.2011.6091562"},{"key":"10_CR14","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M (2014) Generative adversarial networks. arXiv Preprint arXiv: \u2026, 1\u20139. Retrieved from http:\/\/arxiv.org\/abs\/1406.2661"},{"key":"10_CR15","unstructured":"Haloi M (2015) Improved microaneurysm detection using deep neural networks. arXiv:1505.04424 [Cs]"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. NIPS 2014 Deep Learning Workshop, 1\u20139. http:\/\/dx.doi.org\/10.1063\/1.4931082","DOI":"10.1063\/1.4931082"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Hoover AD, Kouznetsova V, Goldbaum M (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging, 19(3):203\u2013210. http:\/\/dx.doi.org\/10.1109\/42.845178","DOI":"10.1109\/42.845178"},{"key":"10_CR18","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. arXiv: 1409.1556v6, 1\u201314"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Kauppi T, Kalesnykiene V, Kamarainen J-K, Lensu L, Sorri I, Raninen A, Voutilainen R, Uusitalo H, Klviinen H, Pietil J (2007) DIARETDB1 diabetic retinopathy database and evaluation protocol. In Proceeding of the 11th Conference on Medical Image Understanding and Analysis. Aberystwyth, Wales, 2007","DOI":"10.5244\/C.21.15"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Kausar N, Abdullah A, Brahim S, Dey N (2014) Ensemble clustering algorithm with supervised classification of clinical data for early diagnosis of coronary artery disease. J Med Imaging Health Informatics, (December). http:\/\/dx.doi.org\/10.1166\/jmihi.2016.1593","DOI":"10.1166\/jmihi.2016.1593"},{"key":"10_CR21","unstructured":"Kim Y-D, Park E, Yoo S, Choi T, Yang L, Shin D (2015) Compression of deep convolutional neural networks for fast and low power mobile applications, 1\u201316. Retrieved from http:\/\/arxiv.org\/abs\/1511.06530"},{"key":"10_CR22","unstructured":"Krachmer JH, Mannis JM, Holland JE (2010) Cornea\u2014Fundamentals diagnosis and management, 3rd edn. Mosby, Maryland Heights"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, 1\u20139. http:\/\/dx.doi.org\/10.1016\/j.protcy.2014.09.007","DOI":"10.1016\/j.protcy.2014.09.007"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Li Q, Feng B, Xie L, Liang P, Zhang H, Wang T (2016) A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans Med Imaging 35(1):109\u2013118. http:\/\/dx.doi.org\/10.1109\/TMI.2015.2457891","DOI":"10.1109\/TMI.2015.2457891"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Liu X, Jiang J, Zhang K, Long E, Cui J, Zhu M et al (2017) Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network. Plos One 12(3):e0168606. http:\/\/dx.doi.org\/10.1371\/journal.pone.0168606","DOI":"10.1371\/journal.pone.0168606"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Maninis KK, Pont-Tuset J, Arbel\u00e1ez P, Van Gool L (2016) Deep retinal image understanding. In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)","DOI":"10.1007\/978-3-319-46723-8_17"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Melinscak M, Prentasic P, Loncaric S (2015) Retinal vessel segmentation using deep neural networks. In: International Conference on Computer Vision Theory and Applications (VISAPP 2015), pp 577\u2013582","DOI":"10.5220\/0005313005770582"},{"key":"10_CR28","unstructured":"Mitchell TM (1997) Machine learning, 1st ed. McGraw-Hill Education, New York"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Mrejen S, Spaide RF (2013) Optical coherence tomography: imaging of the choroid and beyond. Surv Ophthalmol 58(5):387\u2013429. http:\/\/dx.doi.org\/10.1016\/j.survophthal.2012.12.001","DOI":"10.1016\/j.survophthal.2012.12.001"},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Olsen TW, Adelman RA, Flaxel CJ, Folk JC, Pulido JS, Regilo CD, Hyman L (2016) Preferred practice pattern: diabetic retinopathy. Am Acad Ophthalmol http:\/\/www.aao.org\/preferred-practice-pattern\/diab . http:\/\/dx.doi.org\/10.1016\/S0140-6736(09)62124-3","DOI":"10.1016\/S0140-6736(09)62124-3"},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Papernot N, McDaniel P, Jha S, Fredrikson M, Celik ZB, Swami A (2015) The limitations of deep learning in adversarial settings. In: IEEE European Symposium on Security & Privacy, IEEE 2016, Saarbrucken, Germany. arXiv:1511.07528 . http:\/\/dx.doi.org\/10.1109\/EuroSP.2016.36","DOI":"10.1109\/EuroSP.2016.36"},{"key":"10_CR32","doi-asserted-by":"crossref","unstructured":"Prentasic P, Loncaric S (2016) Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion. Comput Methods Programs Biomed 137:281\u2013292. http:\/\/dx.doi.org\/10.1016\/j.cmpb.2016.09.018","DOI":"10.1016\/j.cmpb.2016.09.018"},{"key":"10_CR33","doi-asserted-by":"crossref","unstructured":"Prentasic P, Loncaric S, Vatavuk Z, Bencic G, Subasic M, Petkovic T et al (2013) Diabetic Retinopathy Image Database(DRiDB): a new database for diabetic retinopathy screening programs research. In: 2013 8th International Symposium on Image and Signal Processing and Analysis (Ispa), vol 711, pp 711\u2013716","DOI":"10.1109\/ISPA.2013.6703830"},{"key":"10_CR34","doi-asserted-by":"crossref","unstructured":"Ravi D, Wong C, Deligianni F, Berthelot M, Andreu Perez J, Lo B, Yang G-Z (2016) Deep learning for health informatics. IEEE J Biomed Health Inf 21(1):1\u20131. http:\/\/dx.doi.org\/10.1109\/JBHI.2016.2636665","DOI":"10.1109\/JBHI.2016.2636665"},{"key":"10_CR35","doi-asserted-by":"crossref","unstructured":"Roychowdhury S, Koozekanani D, Parhi K (2013) DREAM: diabetic retinopathy analysis using machine learning. IEEE J Biomed Health Inf PP(99), 1. http:\/\/dx.doi.org\/10.1109\/JBHI.2013.2294635","DOI":"10.1109\/JBHI.2013.2294635"},{"key":"10_CR36","doi-asserted-by":"crossref","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533\u2013536. http:\/\/dx.doi.org\/10.1038\/323533a0","DOI":"10.1038\/323533a0"},{"key":"10_CR37","volume-title":"Artifitial intelligence\u2014A modern approach","author":"S Russell","year":"2003","unstructured":"Russell S, Norvig P (2003) Artifitial intelligence\u2014A modern approach, 2nd edn. Prentice Hall, New Jersey","edition":"2"},{"key":"10_CR38","doi-asserted-by":"crossref","unstructured":"Selig B, Vermeer KA, Rieger B, Hillenaar T, Luengo Hendriks CL (2015) Fully automatic evaluation of the corneal endothelium from in vivo confocal microscopy. BMC Med Imaging 15(1):13. http:\/\/dx.doi.org\/10.1186\/s12880-015-0054-3","DOI":"10.1186\/s12880-015-0054-3"},{"key":"10_CR39","unstructured":"Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y (2013) OverFeat: integrated recognition, localization and detection using convolutional networks. arXiv Preprint arXiv, 1312.6229. Retrieved from http:\/\/arxiv.org\/abs\/1312.6229"},{"key":"10_CR40","doi-asserted-by":"crossref","unstructured":"Sharma K, Virmani J (2017) A decision support system for classification of normal and medical renal disease using ultrasound images: a decision support system for medical renal diseases. Int J Ambient Comput Intell (IJACI) 8(2). https:\/\/doi.org\/10.4018\/IJACI.2017040104","DOI":"10.4018\/IJACI.2017040104"},{"key":"10_CR41","doi-asserted-by":"crossref","unstructured":"Staal JJ, Abramoff MD, Niemeijer M, Viergever MA, Van Ginneken B (2005) Ridge based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23(4):501\u2013509. http:\/\/dx.doi.org\/10.1109\/TMI.2004.825627","DOI":"10.1109\/TMI.2004.825627"},{"key":"10_CR42","doi-asserted-by":"crossref","unstructured":"Sui X, Zhang S, Wei B, Bi H, Wu J, Pan X et al (2017) Choroid segmentation from optical coherence tomography with graph-edge weights learned from deep convolutional neural networks. Neurocomputing (January), 0\u20131. http:\/\/dx.doi.org\/10.1016\/j.neucom.2017.01.023","DOI":"10.1016\/j.neucom.2017.01.023"},{"key":"10_CR43","doi-asserted-by":"crossref","unstructured":"Trucco E, Ruggeri A, Karnowski T, Giancardo L, Chaum E, Hubschman JP et al (2017) Validating retinal fundus image analysis algorithms\u202f: issues and a proposal. IOVS. http:\/\/dx.doi.org\/10.1167\/iovs.12-10347","DOI":"10.1167\/iovs.12-10347"},{"key":"10_CR44","unstructured":"Yanoff M, Duker JS (2014). Ophthalmology. In Ophtalmolgy, 4th edn. Saunders, Elsevier"},{"key":"10_CR45","doi-asserted-by":"crossref","unstructured":"Zhanga Y, Zhanga B, Lua W (2011) Breast cancer classification from histological images with multiple features and random subspace classifier ensemble. In: AIP Conference Proceedings, vol 19(1371). http:\/\/dx.doi.org\/10.1063\/1.3596623","DOI":"10.1063\/1.3596623"},{"key":"10_CR46","doi-asserted-by":"crossref","unstructured":"Zilly J, Buhmann JM, Mahapatra D (2017) Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation. Comput Med Imaging Graph\u00a055:28\u201341. http:\/\/dx.doi.org\/10.1016\/j.compmedimag.2016.07.012","DOI":"10.1016\/j.compmedimag.2016.07.012"}],"container-title":["Lecture Notes in Computational Vision and Biomechanics","Classification in BioApps"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-65981-7_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,5]],"date-time":"2019-10-05T22:15:11Z","timestamp":1570313711000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-65981-7_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,11,14]]},"ISBN":["9783319659800","9783319659817"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-65981-7_10","relation":{},"ISSN":["2212-9391","2212-9413"],"issn-type":[{"type":"print","value":"2212-9391"},{"type":"electronic","value":"2212-9413"}],"subject":[],"published":{"date-parts":[[2017,11,14]]}}}