{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T11:58:33Z","timestamp":1743076713795,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030210731"},{"type":"electronic","value":"9783030210748"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-21074-8_25","type":"book-chapter","created":{"date-parts":[[2019,6,18]],"date-time":"2019-06-18T10:10:14Z","timestamp":1560852614000},"page":"303-308","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["AI-based AMD Analysis: A Review of Recent Progress"],"prefix":"10.1007","author":[{"given":"P.","family":"Burlina","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"N.","family":"Joshi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"N. M.","family":"Bressler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,6,19]]},"reference":[{"issue":"14","key":"25_CR1","doi-asserted-by":"publisher","first-page":"ORSF5","DOI":"10.1167\/iovs.13-12789","volume":"54","author":"Ronald Klein","year":"2013","unstructured":"Klein, R., Klein, B.E.K.: The prevalence of age-related eye diseases and visual impairment in aging: current estimates. Investig. Ophthalmol. Vis. Sci. 54(14) (2013)","journal-title":"Investigative Opthalmology & Visual Science"},{"issue":"3","key":"25_CR2","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1097\/IAE.0000000000000036","volume":"34","author":"R Velez-Montoya","year":"2014","unstructured":"Velez-Montoya, R., Oliver, S.C.N., Olson, J.L., Fine, S.L., Quiroz-Mercado, H., Mandava, N.: Current knowledge and trends in age-related macular degeneration: genetics, epidemiology, and prevention. Retina 34(3), 423\u2013441 (2014)","journal-title":"Retina"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Burlina, P., Freund, D.E., Dupas, B., Bressler, N.: Automatic screening of age-related macular degeneration and retinal abnormalities. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, pp. 3962\u20133966. IEEE (2011)","DOI":"10.1109\/IEMBS.2011.6090984"},{"issue":"5","key":"25_CR4","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.1016\/j.ophtha.2013.11.023","volume":"121","author":"FG Holz","year":"2014","unstructured":"Holz, F.G., Strauss, E.C., Schmitz-Valckenberg, S., van Lookeren Campagne, M.: Geographic atrophy: clinical features and potential therapeutic approaches. Ophthalmology 121(5), 1079\u20131091 (2014)","journal-title":"Ophthalmology"},{"issue":"4","key":"25_CR5","doi-asserted-by":"publisher","first-page":"2318","DOI":"10.1167\/iovs.16-20541","volume":"58","author":"FG Venhuizen","year":"2017","unstructured":"Venhuizen, F.G., et al.: Automated staging of age-related macular degeneration using optical coherence tomography. Investig. Ophthalmol. Vis. Sci. 58(4), 2318\u20132328 (2017)","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Freund, D.E., Bressler, N., Burlina, P.: Automated detection of drusen in the macula. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, pp. 61\u201364. IEEE (2009)","DOI":"10.1109\/ISBI.2009.5192983"},{"key":"25_CR7","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.compbiomed.2015.06.018","volume":"65","author":"AK Feeny","year":"2015","unstructured":"Feeny, A.K., Tadarati, M., Freund, D.E., Bressler, N.M., Burlina, P.: Automated segmentation of geographic atrophy of the retinal epithelium via random forests in AREDS color fundus images. Comput. Biol. Med. 65, 124\u2013136 (2015)","journal-title":"Comput. Biol. Med."},{"issue":"7639","key":"25_CR8","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115\u2013118 (2017)","journal-title":"Nature"},{"issue":"8","key":"25_CR9","doi-asserted-by":"publisher","first-page":"e0184059","DOI":"10.1371\/journal.pone.0184059","volume":"12","author":"P Burlina","year":"2017","unstructured":"Burlina, P., Billings, S., Joshi, N., Albayda, J.: Automated diagnosis of myositis from muscle ultrasound: exploring the use of machine learning and deep learning methods. PloS one 12(8), e0184059 (2017)","journal-title":"PloS one"},{"key":"25_CR10","doi-asserted-by":"crossref","unstructured":"Burlina, P., Freund, D.E., Joshi, N., Wolfson, Y., Bressler, N.M.: Detection of age-related macular degeneration via deep learning. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 184\u2013188. IEEE (2016)","DOI":"10.1109\/ISBI.2016.7493240"},{"key":"25_CR11","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1001\/jamaophthalmol.2017.3782","volume":"135","author":"P Burlina","year":"2017","unstructured":"Burlina, P., Joshi, N., Pekala, M., Pacheco, K., Freund, D.E., Bressler, N.M.: Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophtalmol. 135, 1170\u20131176 (2017)","journal-title":"JAMA Ophtalmol."},{"key":"25_CR12","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.compbiomed.2017.01.018","volume":"82","author":"P Burlina","year":"2017","unstructured":"Burlina, P., Pacheco, K.D., Joshi, N., Freund, D.E., Bressler, N.M.: Comparing humans and deep learning performance for grading AMD: a study in using universal deep features and transfer learning for automated AMD analysis. Compu. Biol. Med. 82, 80\u201386 (2017)","journal-title":"Compu. Biol. Med."},{"key":"25_CR13","doi-asserted-by":"publisher","first-page":"1359","DOI":"10.1001\/jamaophthalmol.2018.4118","volume":"136","author":"P Burlina","year":"2018","unstructured":"Burlina, P., Joshi, N., Pacheco, K.D., Freund, D.E., Kong, J., Bressler, N.M.: Use of deep learning for detailed severity characterization and estimation of 5-year risk among patients with age-related macular degeneration. JAMA Ophthalmol. 136, 1359\u20131366 (2018)","journal-title":"JAMA Ophthalmol."},{"key":"25_CR14","doi-asserted-by":"publisher","first-page":"1305","DOI":"10.1001\/jamaophthalmol.2018.3799","volume":"136","author":"P Burlina","year":"2018","unstructured":"Burlina, P., Joshi, N., Pacheco, K.D., Freund, D.E., Kong, J., Bressler, N.M.: Utility of deep learning methods for referability classification of age-related macular degeneration. JAMA Ophthalmol. 136, 1305\u20131307 (2018)","journal-title":"JAMA Ophthalmol."},{"issue":"22","key":"25_CR15","doi-asserted-by":"publisher","first-page":"2211","DOI":"10.1001\/jama.2017.18152","volume":"318","author":"DSW Ting","year":"2017","unstructured":"Ting, D.S.W., et al.: Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 318(22), 2211\u20132223 (2017)","journal-title":"JAMA"},{"key":"25_CR16","doi-asserted-by":"crossref","unstructured":"Age-Related Eye Disease Study Research Group et al. 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(5), 668\u2013681 (2001)","DOI":"10.1016\/S0002-9394(01)01218-1"},{"issue":"5","key":"25_CR17","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1038\/s41591-018-0029-3","volume":"24","author":"DSW Ting","year":"2018","unstructured":"Ting, D.S.W., Liu, Y., Burlina, P., Xu, X., Bressler, N.M., Wong, T.Y.: AI for medical imaging goes deep. Nat. Med. 24(5), 539 (2018)","journal-title":"Nat. Med."},{"key":"25_CR18","doi-asserted-by":"publisher","first-page":"1410","DOI":"10.1016\/j.ophtha.2018.02.037","volume":"125","author":"F Grassmann","year":"2018","unstructured":"Grassmann, F., et al.: A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography. Ophthalmology 125, 1410\u20131420 (2018)","journal-title":"Ophthalmology"},{"issue":"3","key":"25_CR19","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1001\/jamaophthalmol.2018.6156","volume":"137","author":"PM Burlina","year":"2019","unstructured":"Burlina, P.M., Joshi, N., Pacheco, K.D., Liu, T.Y.A., Bressler, N.M.: Assessment of deep generative models for high-resolution synthetic retinal image generation of age-related macular degeneration. JAMA Ophthalmol. 137(3), 258 (2019)","journal-title":"JAMA Ophthalmol."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2018 Workshops"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-21074-8_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T00:01:15Z","timestamp":1562544075000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-21074-8_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030210731","9783030210748"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-21074-8_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"19 June 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Perth, WA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/accv2018.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"979","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"274","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}