{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T07:29:54Z","timestamp":1769844594252,"version":"3.49.0"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164330","type":"print"},{"value":"9783031164347","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\/978-3-031-16434-7_60","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T15:03:08Z","timestamp":1663254188000},"page":"625-634","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["TINC: Temporally Informed Non-contrastive Learning for\u00a0Disease Progression Modeling in\u00a0Retinal OCT Volumes"],"prefix":"10.1007","author":[{"given":"Taha","family":"Emre","sequence":"first","affiliation":[]},{"given":"Arunava","family":"Chakravarty","sequence":"additional","affiliation":[]},{"given":"Antoine","family":"Rivail","sequence":"additional","affiliation":[]},{"given":"Sophie","family":"Riedl","sequence":"additional","affiliation":[]},{"given":"Ursula","family":"Schmidt-Erfurth","sequence":"additional","affiliation":[]},{"given":"Hrvoje","family":"Bogunovi\u0107","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"60_CR1","doi-asserted-by":"crossref","unstructured":"Azizi, S., et al.: Big self-supervised models advance medical image classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3478\u20133488 (2021)","DOI":"10.1109\/ICCV48922.2021.00346"},{"key":"60_CR2","doi-asserted-by":"publisher","unstructured":"Banerjee, I., et al.: Prediction of age-related macular degeneration disease using a sequential deep learning approach on longitudinal sd-oct imaging biomarkers. Sci. Rep. 10(1), 15434 (2020). https:\/\/doi.org\/10.1038\/s41598-020-72359-y","DOI":"10.1038\/s41598-020-72359-y"},{"key":"60_CR3","unstructured":"Bardes, A., Ponce, J., LeCun, Y.: VICReg: variance-invariance-covariance regularization for self-supervised learning. In: International Conference on Learning Representations (2022)"},{"issue":"15","key":"60_CR4","doi-asserted-by":"publisher","first-page":"1900","DOI":"10.1001\/jama.291.15.1900","volume":"291","author":"NM Bressler","year":"2004","unstructured":"Bressler, N.M.: Age-related macular degeneration is the leading cause of blindness. JAMA 291(15), 1900\u20131901 (2004). https:\/\/doi.org\/10.1001\/jama.291.15.1900","journal-title":"JAMA"},{"key":"60_CR5","first-page":"9912","volume":"33","author":"M Caron","year":"2020","unstructured":"Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Adv. Neural. Inf. Process. Syst. 33, 9912\u20139924 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"60_CR6","doi-asserted-by":"crossref","unstructured":"Chen, X., He, K.: Exploring simple siamese representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15750\u201315758 (2021)","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"60_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1007\/978-3-030-87237-3_60","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Y Chen","year":"2021","unstructured":"Chen, Y., et al.: USCL: Pretraining deep ultrasound image diagnosis model through video contrastive representation learning. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 627\u2013637. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87237-3_60"},{"key":"60_CR8","unstructured":"Ermolov, A., Siarohin, A., Sangineto, E., Sebe, N.: Whitening for self-supervised representation learning. In: International Conference on Machine Learning, pp. 3015\u20133024. PMLR (2021)"},{"key":"60_CR9","first-page":"21271","volume":"33","author":"JB Grill","year":"2020","unstructured":"Grill, J.B., et al.: Bootstrap your own latent-a new approach to self-supervised learning. Adv. Neural. Inf. Process. Syst. 33, 21271\u201321284 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"60_CR10","unstructured":"Jing, L., Vincent, P., LeCun, Y., Tian, Y.: Understanding dimensional collapse in contrastive self-supervised learning. In: International Conference on Learning Representations (2022)"},{"key":"60_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1007\/978-3-030-87196-3_4","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"H Li","year":"2021","unstructured":"Li, H., et al.: Imbalance-aware self-supervised learning for 3D radiomic representations. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 36\u201346. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87196-3_4"},{"key":"60_CR12","unstructured":"van den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding (2019)"},{"key":"60_CR13","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"},{"issue":"2","key":"60_CR14","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1167\/iovs.18-25325","volume":"60","author":"DB Russakoff","year":"2019","unstructured":"Russakoff, D.B., Lamin, A., Oakley, J.D., Dubis, A.M., Sivaprasad, S.: Deep learning for prediction of amd progression: a pilot study. Invest. Ophthalmol. Visual Sci. 60(2), 712\u2013722 (2019)","journal-title":"Invest. Ophthalmol. Visual Sci."},{"issue":"8","key":"60_CR15","doi-asserted-by":"publisher","first-page":"3199","DOI":"10.1167\/iovs.18-24106","volume":"59","author":"U Schmidt-Erfurth","year":"2018","unstructured":"Schmidt-Erfurth, U., et al.: Prediction of individual disease conversion in early amd using artificial intelligence. Invest. Ophthalmol. Visual Sci. 59(8), 3199\u20133208 (2018)","journal-title":"Invest. Ophthalmol. Visual Sci."},{"issue":"2","key":"60_CR16","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.oret.2020.06.026","volume":"5","author":"Z Wu","year":"2021","unstructured":"Wu, Z., Bogunovi\u0107, H., Asgari, R., Schmidt-Erfurth, U., Guymer, R.H.: Predicting progression of age-related macular degeneration using oct and fundus photography. Ophthalmol. Retina 5(2), 118\u2013125 (2021). https:\/\/doi.org\/10.1016\/j.oret.2020.06.026","journal-title":"Ophthalmol. Retina"},{"issue":"2","key":"60_CR17","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1038\/s42256-020-0154-9","volume":"2","author":"Q Yan","year":"2020","unstructured":"Yan, Q., et al.: Deep-learning-based prediction of late age-related macular degeneration progression. Nat. Mach. intell. 2(2), 141\u2013150 (2020)","journal-title":"Nat. Mach. intell."},{"key":"60_CR18","doi-asserted-by":"publisher","unstructured":"Yang, J., et al.: Two-year risk of exudation in eyes with nonexudative age-related macular degeneration and subclinical neovascularization detected with swept source optical coherence tomography angiography. Am. J. Ophthalmol. 208, 1\u201311 (2019). https:\/\/doi.org\/10.1016\/j.ajo.2019.06.017","DOI":"10.1016\/j.ajo.2019.06.017"},{"issue":"6","key":"60_CR19","doi-asserted-by":"publisher","first-page":"892","DOI":"10.1038\/s41591-020-0867-7","volume":"26","author":"J Yim","year":"2020","unstructured":"Yim, J., et al.: Predicting conversion to wet age-related macular degeneration using deep learning. Nat. Med. 26(6), 892\u2013899 (2020)","journal-title":"Nat. Med."},{"key":"60_CR20","unstructured":"Zbontar, J., Jing, L., Misra, I., LeCun, Y., Deny, S.: Barlow twins: self-supervised learning via redundancy reduction. In: International Conference on Machine Learning, pp. 12310\u201312320. PMLR (2021)"},{"issue":"5","key":"60_CR21","doi-asserted-by":"publisher","first-page":"3202","DOI":"10.1167\/iovs.14-15669","volume":"56","author":"L Zhang","year":"2015","unstructured":"Zhang, L., et al.: Validity of Automated Choroidal Segmentation in SS-OCT and SD-OCT. Investigative Ophthalmol. Visual Sci. 56(5), 3202\u20133211 (2015). https:\/\/doi.org\/10.1167\/iovs.14-15669","journal-title":"Investigative Ophthalmol. Visual Sci."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16434-7_60","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T11:50:20Z","timestamp":1710330620000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16434-7_60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164330","9783031164347"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16434-7_60","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":"16 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","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":"18 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":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","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 Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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":"5","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)"}}]}}