{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T23:47:10Z","timestamp":1767138430977,"version":"build-2238731810"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031165245","type":"print"},{"value":"9783031165252","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16525-2_5","type":"book-chapter","created":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T19:03:00Z","timestamp":1663182180000},"page":"43-52","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Detection of\u00a0Diabetic Retinopathy Using Longitudinal Self-supervised Learning"],"prefix":"10.1007","author":[{"given":"Rachid","family":"Zeghlache","sequence":"first","affiliation":[]},{"given":"Pierre-Henri","family":"Conze","sequence":"additional","affiliation":[]},{"given":"Mostafa El Habib","family":"Daho","sequence":"additional","affiliation":[]},{"given":"Ramin","family":"Tadayoni","sequence":"additional","affiliation":[]},{"given":"Pascal","family":"Massin","sequence":"additional","affiliation":[]},{"given":"B\u00e9atrice","family":"Cochener","sequence":"additional","affiliation":[]},{"given":"Gwenol\u00e9","family":"Quellec","sequence":"additional","affiliation":[]},{"given":"Mathieu","family":"Lamard","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","unstructured":"Albelwi, S.: Survey on self-supervised learning: auxiliary pretext tasks and contrastive learning methods in imaging. Entropy 24(4) (2022). https:\/\/doi.org\/10.3390\/e24040551, https:\/\/www.mdpi.com\/1099-4300\/24\/4\/551","DOI":"10.3390\/e24040551"},{"key":"5_CR2","doi-asserted-by":"publisher","unstructured":"Chamard, C., et al.: Ten-year incidence and assessment of safe screening intervals for diabetic retinopathy: the OPHDIAT study. Br. J. Ophthalmol. 105(3), 432\u2013439 (2020). https:\/\/doi.org\/10.1136\/bjophthalmol-2020-316030","DOI":"10.1136\/bjophthalmol-2020-316030"},{"key":"5_CR3","doi-asserted-by":"publisher","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020). https:\/\/doi.org\/10.48550\/ARXIV.2002.05709, https:\/\/arxiv.org\/abs\/2002.05709","DOI":"10.48550\/ARXIV.2002.05709"},{"key":"5_CR4","doi-asserted-by":"publisher","first-page":"102115","DOI":"10.1016\/j.bspc.2020.102115","volume":"62","author":"S Gayathri","year":"2020","unstructured":"Gayathri, S., Gopi, V.P., Palanisamy, P.: A lightweight CNN for diabetic retinopathy classification from fundus images. Biomed. Signal Process. Control 62, 102115 (2020)","journal-title":"Biomed. Signal Process. Control"},{"key":"5_CR5","doi-asserted-by":"publisher","unstructured":"Huang, Y., Lin, L., Cheng, P., Lyu, J., Tang, X.: Lesion-based contrastive learning for diabetic retinopathy grading from fundus images (2021). https:\/\/doi.org\/10.48550\/ARXIV.2107.08274, https:\/\/arxiv.org\/abs\/2107.08274","DOI":"10.48550\/ARXIV.2107.08274"},{"key":"5_CR6","unstructured":"Liu, X., et al.: Self-supervised learning: generative or contrastive. arXiv preprint arXiv:2006.08218 vol. 1, no. 2 (2020)"},{"key":"5_CR7","doi-asserted-by":"publisher","unstructured":"Massin, P., et al.: Ophdiat: a telemedical network screening system for diabetic retinopathy in the \u00cele-de-france. Diab. Metab. 34, 227\u201334 (2008). https:\/\/doi.org\/10.1016\/j.diabet.2007.12.006","DOI":"10.1016\/j.diabet.2007.12.006"},{"key":"5_CR8","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.diabres.2017.03.024","volume":"128","author":"K Ogurtsova","year":"2017","unstructured":"Ogurtsova, K., et al.: IDF diabetes atlas: global estimates for the prevalence of diabetes for 2015 and 2040. Diab. Res. Clin. Pract. 128, 40\u201350 (2017)","journal-title":"Diab. Res. Clin. Pract."},{"key":"5_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1007\/978-3-030-87196-3_8","volume-title":"Medical Image Computing and Computer Assisted Intervention","author":"J Ouyang","year":"2021","unstructured":"Ouyang, J., et al.: Self-supervised longitudinal neighbourhood embedding. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 80\u201389. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87196-3_8"},{"key":"5_CR10","doi-asserted-by":"publisher","unstructured":"Pratt, H., Coenen, F., Broadbent, D.M., Harding, S.P., Zheng, Y.: Convolutional neural networks for diabetic retinopathy. Procedia Comput. Sci. 90, 200\u2013205 (2016). https:\/\/doi.org\/10.1016\/j.procs.2016.07.014, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1877050916311929, 20th Conference on Medical Image Understanding and Analysis (MIUA 2016)","DOI":"10.1016\/j.procs.2016.07.014"},{"key":"5_CR11","doi-asserted-by":"publisher","unstructured":"Quellec, G., Charri\u00e8re, K., Boudi, Y., Cochener, B., Lamard, M.: Deep image mining for diabetic retinopathy screening. Med. Image Anal.39, 178\u2013193 (2017). https:\/\/doi.org\/10.1016\/j.media.2017.04.012, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S136184151730066X","DOI":"10.1016\/j.media.2017.04.012"},{"key":"5_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1007\/978-3-030-32281-6_5","volume-title":"Predictive Intelligence in Medicine","author":"A Rivail","year":"2019","unstructured":"Rivail, A., et al.: Modeling disease progression in retinal OCTs with longitudinal self-supervised learning. In: Rekik, I., Adeli, E., Park, S.H. (eds.) PRIME 2019. LNCS, vol. 11843, pp. 44\u201352. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32281-6_5"},{"key":"5_CR13","doi-asserted-by":"publisher","unstructured":"Robin, X., et al.: pROC: an open-source package for r and s to analyze and compare ROC curves. BMC Bioinf. 12(1) (2011). https:\/\/doi.org\/10.1186\/1471-2105-12-77","DOI":"10.1186\/1471-2105-12-77"},{"key":"5_CR14","doi-asserted-by":"publisher","unstructured":"Saeedi, P., et al.: Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the international diabetes federation diabetes atlas, 9th edn. Diab. Res. Clin. Pract. 157, 107843 (2019). https:\/\/doi.org\/10.1016\/j.diabres.2019.107843","DOI":"10.1016\/j.diabres.2019.107843"},{"key":"5_CR15","doi-asserted-by":"publisher","unstructured":"Saha, S.K., Xiao, D., Bhuiyan, A., Wong, T.Y., Kanagasingam, Y.: Color fundus image registration techniques and applications for automated analysis of diabetic retinopathy progression: a review. Biomed. Sig. Process. Control 47, 288\u2013302 (2019). https:\/\/doi.org\/10.1016\/j.bspc.2018.08.034","DOI":"10.1016\/j.bspc.2018.08.034"},{"key":"5_CR16","doi-asserted-by":"publisher","unstructured":"Vernhet, P., Durrleman, S.: Longitudinal self-supervision to disentangle inter-patient variability, pp. 231\u2013241 (2021). https:\/\/doi.org\/10.1007\/978-3-030-87196-3","DOI":"10.1007\/978-3-030-87196-3"},{"key":"5_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/978-3-030-87000-3_2","volume-title":"Ophthalmic Medical Image Analysis","author":"Y Yan","year":"2021","unstructured":"Yan, Y., et al.: Longitudinal detection of diabetic retinopathy early severity grade changes using deep learning. In: Fu, H., Garvin, M.K., MacGillivray, T., Xu, Y., Zheng, Y. (eds.) OMIA 2021. LNCS, vol. 12970, pp. 11\u201320. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87000-3_2"},{"key":"5_CR18","doi-asserted-by":"publisher","unstructured":"Zhao, Q., Liu, Z., Adeli, E., Pohl, K.M.: Longitudinal self-supervised learning. Med. Image Anal. 71 (2021). https:\/\/doi.org\/10.1016\/j.media.2021.102051","DOI":"10.1016\/j.media.2021.102051"}],"updated-by":[{"DOI":"10.1007\/978-3-031-16525-2_21","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T00:00:00Z","timestamp":1663200000000}}],"container-title":["Lecture Notes in Computer Science","Ophthalmic Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16525-2_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T03:05:52Z","timestamp":1671591952000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16525-2_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031165245","9783031165252"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16525-2_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"15 September 2022","order":2,"name":"change_date","label":"Change Date","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Correction","order":3,"name":"change_type","label":"Change Type","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"In an older version of this chapter, the title was incomplete. This has been corrected.","order":4,"name":"change_details","label":"Change Details","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"OMIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Ophthalmic Medical Image Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"omia2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/omia9\/home","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":"CMT system","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33","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":"20","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":"61% - 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":"3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}