{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T12:09:06Z","timestamp":1773490146807,"version":"3.50.1"},"publisher-location":"Wiesbaden","reference-count":18,"publisher":"Springer Fachmedien Wiesbaden","isbn-type":[{"value":"9783658510992","type":"print"},{"value":"9783658511005","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-658-51100-5_11","type":"book-chapter","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T14:07:25Z","timestamp":1773238045000},"page":"63-69","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Revealing Eye-dentity"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4168-9647","authenticated-orcid":false,"given":"Marc S.","family":"Seibel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2039-423X","authenticated-orcid":false,"given":"Nele S.","family":"Br\u00fcgge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2024-2958","authenticated-orcid":false,"given":"Timo","family":"Kepp","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5609-9250","authenticated-orcid":false,"given":"Bennet","family":"Kahrs","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6804-5587","authenticated-orcid":false,"given":"Jan","family":"Ehrhardt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heinz","family":"Handels","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,12]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Katuru A, Chung IY, Majid I, Shen LQ, Wang M. Deep learning with disc photos or OCT scans in glaucoma detection. Ophthalmol Sci. 2025;5(6).","DOI":"10.1016\/j.xops.2025.100877"},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Nandy Pal M, Roy S, Banerjee M. Content based retrieval of retinal OCT scans using twin CNN. S\u00afadhan\u00afa. 2021;46(3):174.","DOI":"10.1007\/s12046-021-01701-5"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Packh\u00e4user K, G\u00fcndel S, M\u00fcnster N, Syben C, Christlein V, Maier A. Deep learningbased patient re-identification is able to exploit the biometric nature of medical chest X-ray data. Sci Rep. 2022;12(1):14851.","DOI":"10.1038\/s41598-022-19045-3"},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Tian Y, Ji K, Zhang R, Jiang Y, Li C, Wang X et al. Towards all-in-one medical image re-identification. Proc IEEE\/CVF CVPR. 2025.","DOI":"10.1109\/CVPR52734.2025.02866"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Ueda Y, Morishita J. Patient identification based on deep metric learning for preventing human errors in follow-up X-ray examinations. J Digit Imaging. 2023;36(5):1941\u201353.","DOI":"10.1007\/s10278-023-00850-9"},{"key":"11_CR6","unstructured":"Puglisi L, Eshaghi A, Parker G, Barkhof F, Alexander DC, Ravi D. DeepBrainPrint: a novel contrastive framework for brain MRI re-identification. Proc MIDL:716\u201329."},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Nebbia G, Kumar S, McNamara SM, Bridge C, Campbell JP, Chiang MF et al. Reidentification of patients from imaging features extracted by foundation models. NPJ Digit Med. 2025;8(1):469.","DOI":"10.1038\/s41746-025-01801-0"},{"key":"11_CR8","unstructured":"Oquab M, Darcet T, Moutakanni T, Vo H, Szafraniec M, Khalidov V et al. DINOv2: learning robust visual features without supervision. arXiv: 2304.07193. 2023."},{"key":"11_CR9","unstructured":"Sim\u00e9oni O, Vo HV, Seitzer M, Baldassarre F, Oquab M, Jose C et al. DINOv3. arXiv: 2508.10104. 2025."},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Zhou Y, Chia MA, Wagner SK, Ayhan MS, Williamson DJ, Struyven RR et al. A foundation model for generalizable disease detection from retinal images. Nature. 2023;622(7981):156\u201363.","DOI":"10.1038\/s41586-023-06555-x"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Kuo D, Gao Q, Patel D, Pajic M, Hadziahmetovic M. How foundational is the retina foundation model? estimating RETFound\u2019s label efficiency on binary classification of normal versus abnormal OCT images. Ophthalmol Sci. 2025;5(3):100707.","DOI":"10.1016\/j.xops.2025.100707"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Sudkamp H, Koch P, Spahr H, Hillmann D, Franke G, M\u00fcnst M et al. In-vivo retinal imaging with off-axis full-field time-domain optical coherence tomography. Opt Lett. 2016;41(21):4987.","DOI":"10.1364\/OL.41.004987"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Seibel MS, Rowedder M, Andresen J, Neumann T, Neffin R, Sudkamp H et al. Enhancing retinal SELFF-OCT image quality: a deep-learning-based pipeline. Proc SPIE MI IP. 2025;13406.","DOI":"10.1117\/12.3047094"},{"key":"11_CR14","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton G. A simple framework for contrastive learning of visual representations. Proc ICML:1597\u2013607."},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Dwork C. Differential privacy. Aut Lang Program. 2006:1\u201312.","DOI":"10.1007\/11787006_1"},{"key":"11_CR16","unstructured":"Li K, Gong C, Li Z, Zhao Y, Hou X,Wang T. PrivImage: differentially private synthetic image generation using diffusion models with semantic-aware pretraining. Proc USENIX SEC. 2024:4837\u201354."},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Nam KT, Yun C, Seo M, Ahn S, Oh J. Comparison of retinal thickness measurements among four different optical coherence tomography devices. Sci Rep. 2024;14(1):3560.","DOI":"10.1038\/s41598-024-54109-6"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"K\u00f6se C, \u0130ki\u02d9ba\u015f C. A personal identification system using retinal vasculature in retinal fundus images. Expert Syst Appl. 2011;38(11):13670\u201381.","DOI":"10.1016\/j.eswa.2011.04.141"}],"container-title":["Informatik aktuell","Bildverarbeitung f\u00fcr die Medizin 2026"],"original-title":[],"language":"de","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-658-51100-5_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T11:04:41Z","timestamp":1773486281000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-658-51100-5_11"}},"subtitle":["Foundation Models Enable Re-identification from Retinal OCT"],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783658510992","9783658511005"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-658-51100-5_11","relation":{},"ISSN":["1431-472X","2628-8958"],"issn-type":[{"value":"1431-472X","type":"print"},{"value":"2628-8958","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"12 March 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BVM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"BVM Workshop","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"L\u00fcbeck","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Deutschland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 March 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 March 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bvm2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.bvm-conf.org\/de\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}