{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T09:37:07Z","timestamp":1776850627383,"version":"3.51.2"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051813","type":"print"},{"value":"9783032051820","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T00:00:00Z","timestamp":1758153600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T00:00:00Z","timestamp":1758153600000},"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\/978-3-032-05182-0_55","type":"book-chapter","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T00:01:11Z","timestamp":1758153671000},"page":"564-573","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Self-Propagative Multi-Task Learning for\u00a0Predicting Cardiometabolic Risk Factors"],"prefix":"10.1007","author":[{"given":"Seonghyeon","family":"Ko","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huigyu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junghyun","family":"Bum","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Duc-Tai","family":"Le","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyunseung","family":"Choo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,18]]},"reference":[{"key":"55_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12887-015-0500-y","volume":"15","author":"JC Aristizabal","year":"2015","unstructured":"Aristizabal, J.C., Barona, J., Hoyos, M., Ruiz, M., Mar\u00edn, C.: Association between anthropometric indices and cardiometabolic risk factors in pre-school children. BMC Pediatr. 15, 1\u20138 (2015)","journal-title":"BMC Pediatr."},{"issue":"4","key":"55_CR2","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1017\/S1368980008002474","volume":"12","author":"AS Can","year":"2009","unstructured":"Can, A.S., Bersot, T.P., G\u00f6nen, M.: Anthropometric indices and their relationship with cardiometabolic risk factors in a sample of Turkish adults. Public Health Nutr. 12(4), 538\u2013546 (2009)","journal-title":"Public Health Nutr."},{"issue":"8","key":"55_CR3","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1016\/S2213-8587(14)70102-0","volume":"2","author":"G Danaei","year":"2014","unstructured":"Danaei, G., et al.: Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: a comparative risk assessment. Lancet Diabetes Endocrinol. 2(8), 634 (2014)","journal-title":"Lancet Diabetes Endocrinol."},{"issue":"52","key":"55_CR4","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000036763","volume":"102","author":"NE Ezinne","year":"2023","unstructured":"Ezinne, N.E., Roodal, D., Ekemiri, K.K., Persad, T., Mashige, K.P.: Ocular parameters and anthropometry in indo-trinidadians. Medicine 102(52), e36763 (2023)","journal-title":"Medicine"},{"issue":"1","key":"55_CR5","doi-asserted-by":"publisher","first-page":"9432","DOI":"10.1038\/s41598-020-65794-4","volume":"10","author":"N Gerrits","year":"2020","unstructured":"Gerrits, N., et al.: Age and sex affect deep learning prediction of cardiometabolic risk factors from retinal images. Sci. Rep. 10(1), 9432 (2020)","journal-title":"Sci. Rep."},{"issue":"7","key":"55_CR6","doi-asserted-by":"publisher","first-page":"1714","DOI":"10.3390\/diagnostics12071714","volume":"12","author":"NC Khan","year":"2022","unstructured":"Khan, N.C., et al.: Predicting systemic health features from retinal fundus images using transfer-learning-based artificial intelligence models. Diagnostics 12(7), 1714 (2022)","journal-title":"Diagnostics"},{"key":"55_CR7","doi-asserted-by":"crossref","unstructured":"Kidy, F.F., et al.: Associations between anthropometric measurements and cardiometabolic risk factors in white European and south Asian adults in the United Kingdom. In: Mayo Clinic Proceedings, vol.\u00a092, pp. 925\u2013933. Elsevier (2017)","DOI":"10.1016\/j.mayocp.2017.02.009"},{"key":"55_CR8","doi-asserted-by":"crossref","unstructured":"Kim, H.T., et al.: Relationships between anthropometric measurements and intraocular pressure: the Korea national health and nutrition examination survey. Am. J. Ophthalmol. 173, 23\u201333 (2017)","DOI":"10.1016\/j.ajo.2016.09.031"},{"key":"55_CR9","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"16","key":"55_CR10","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.118.010870","volume":"8","author":"J Liu","year":"2019","unstructured":"Liu, J., et al.: Predictive values of anthropometric measurements for cardiometabolic risk factors and cardiovascular diseases among 44 048 Chinese. J. Am. Heart Assoc. 8(16), e010870 (2019)","journal-title":"J. Am. Heart Assoc."},{"key":"55_CR11","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976\u201311986 (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"55_CR12","doi-asserted-by":"crossref","unstructured":"P\u00f6lsterl, S., Guti\u00e9rrez-Becker, B., Sarasua, I.: Abhijit guha roy, and christian wachinger artificial intelligence in medical imaging (ai-med), department of child and adolescent psychiatry, ludwig maximilian universit\u00e4t, Munich, Germany. Adolescent Brain Cognitive Development Neurocognitive Prediction: First Challenge, ABCD-NP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, 13 October 2019, Proceedings, vol. 11791, p. 99 (2019)","DOI":"10.1007\/978-3-030-31901-4_12"},{"issue":"3","key":"55_CR13","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1038\/s41551-018-0195-0","volume":"2","author":"R Poplin","year":"2018","unstructured":"Poplin, R., et al.: Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat. Biomed. Eng. 2(3), 158\u2013164 (2018)","journal-title":"Nat. Biomed. Eng."},{"key":"55_CR14","unstructured":"Speechly, C., Bignell, N., Turner, M.: Sphygmomanometer calibration: why, how and how often? Aust. Family Phys. 36(10) (2007)"},{"key":"55_CR15","doi-asserted-by":"crossref","unstructured":"Stevens, S.L., et al.: Blood pressure variability and cardiovascular disease: systematic review and meta-analysis. BMJ 354 (2016)","DOI":"10.1136\/bmj.i4098"},{"key":"55_CR16","doi-asserted-by":"publisher","unstructured":"Suter, Y., Knecht, U., Wiest, R., Reyes, M.: Overall survival prediction for glioblastoma on pre-treatment MRI using robust radiomics and priors. In: Crimi, A., Bakas, S. (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. LNCS, vol. 12658, pp. 307\u2013317. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-72084-1_28","DOI":"10.1007\/978-3-030-72084-1_28"},{"key":"55_CR17","doi-asserted-by":"crossref","unstructured":"Vanuzzo, D., Pilotto, L., Mirolo, R., Pirelli, S.: Cardiovascular risk and cardiometabolic risk: an epidemiological evaluation. Giornale Italiano di Cardiologia (2006) 9(4 Suppl 1), 6S\u201317S (2008)","DOI":"10.1714\/669.7808"},{"key":"55_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/978-3-030-87193-2_6","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Y Zhou","year":"2021","unstructured":"Zhou, Y., Yu, H., Shi, H.: Study group learning: improving retinal vessel segmentation trained with noisy labels. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 57\u201367. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_6"},{"issue":"1","key":"55_CR19","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1016\/B978-0-12-336156-1.50061-6","volume":"4","author":"KJ Zuiderveld","year":"1994","unstructured":"Zuiderveld, K.J., et al.: Contrast limited adaptive histogram equalization. Graphics Gems 4(1), 474\u2013485 (1994)","journal-title":"Graphics Gems"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05182-0_55","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T08:36:59Z","timestamp":1776847019000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05182-0_55"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,18]]},"ISBN":["9783032051813","9783032051820"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05182-0_55","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,18]]},"assertion":[{"value":"18 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}