{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T18:38:28Z","timestamp":1779907108475,"version":"3.53.1"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031845246","type":"print"},{"value":"9783031845253","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-84525-3_19","type":"book-chapter","created":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T11:00:59Z","timestamp":1744628459000},"page":"223-234","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting Stroke Through Retinal Graphs and\u00a0Multimodal Self-supervised Learning"],"prefix":"10.1007","author":[{"given":"Yuqing","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bastian","family":"Wittmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olga","family":"Demler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bjoern","family":"Menze","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neda","family":"Davoudi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,13]]},"reference":[{"issue":"1","key":"19_CR1","first-page":"3563","volume":"15","author":"G Alain","year":"2014","unstructured":"Alain, G., Bengio, Y.: What regularized auto-encoders learn from the data-generating distribution. J. Mach. Learn. Res. 15(1), 3563\u20133593 (2014)","journal-title":"J. Mach. Learn. Res."},{"issue":"8","key":"19_CR2","first-page":"4668","volume":"64","author":"L Arnould","year":"2023","unstructured":"Arnould, L., Soumare, A., Delcourt, C., Helmer, C., Creuzot-Garcher, C., Debette, S.: The retinal vascular network with Singapore I vessel assessment (siva) software and brain magnetic resonance imaging markers of cerebral small vessel disease in the elderly: the montrachet study. Invest. Ophthalmol. Vis. Sci. 64(8), 4668\u20134668 (2023)","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"key":"19_CR3","doi-asserted-by":"publisher","unstructured":"Bumgarner, J.R., Nelson, R.J.: Open-source analysis and visualization of segmented vasculature datasets with vesselvio. Cell Rep. Methods 2(4), 100189 (2022). https:\/\/doi.org\/10.1016\/j.crmeth.2022.100189, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2667237522000443","DOI":"10.1016\/j.crmeth.2022.100189"},{"key":"19_CR4","doi-asserted-by":"publisher","first-page":"2456550","DOI":"10.1155\/2022\/2456550","volume":"2022","author":"L Cui","year":"2022","unstructured":"Cui, L., et al.: Deep learning in ischemic stroke imaging analysis: a comprehensive review. BioMed Res. Int. 2022, 2456550 (2022)","journal-title":"BioMed Res. Int."},{"issue":"1","key":"19_CR5","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1038\/s42256-021-00427-7","volume":"4","author":"A Diaz-Pinto","year":"2022","unstructured":"Diaz-Pinto, A., et al.: Predicting myocardial infarction through retinal scans and minimal personal information. Nat. Mach. Intell. 4(1), 55\u201361 (2022)","journal-title":"Nat. Mach. Intell."},{"key":"19_CR6","unstructured":"Dosovitskiy, A., et al.: An image is worth 16$$\\times $$16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=YicbFdNTTy"},{"issue":"1","key":"19_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-021-04262-w","volume":"22","author":"D Drees","year":"2021","unstructured":"Drees, D., Scherzinger, A., H\u00e4gerling, R., Kiefer, F., Jiang, X.: Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets. BMC Bioinf. 22(1), 1\u201328 (2021)","journal-title":"BMC Bioinf."},{"issue":"3","key":"19_CR8","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TMI.2022.3218720","volume":"42","author":"W Gong","year":"2022","unstructured":"Gong, W., Bai, S., Zheng, Y.Q., Smith, S.M., Beckmann, C.F.: Supervised phenotype discovery from multimodal brain imaging. IEEE Trans. Med. Imaging 42(3), 834\u2013849 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Hager, P., Menten, M.J., Rueckert, D.: Best of both worlds: multimodal contrastive learning with tabular and imaging data. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 23924\u201323935 (2023)","DOI":"10.1109\/CVPR52729.2023.02291"},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2015). https:\/\/api.semanticscholar.org\/CorpusID:206594692","DOI":"10.1109\/CVPR.2016.90"},{"key":"19_CR11","doi-asserted-by":"crossref","unstructured":"Ji, C., et\u00a0al.: Predicting cerebral small vessel disease through retinal scans and demographic data with bayesian feature selection. In: Medical Imaging 2024: Computer-Aided Diagnosis, vol. 12927, pp. 830\u2013837. SPIE (2024)","DOI":"10.1117\/12.3006453"},{"issue":"3","key":"19_CR12","doi-asserted-by":"publisher","first-page":"829","DOI":"10.3390\/jcm13030829","volume":"13","author":"RL Kellner","year":"2024","unstructured":"Kellner, R.L., et al.: The eye as the window to the heart: optical coherence tomography angiography biomarkers as indicators of cardiovascular disease. J. Clin. Med. 13(3), 829 (2024)","journal-title":"J. Clin. Med."},{"issue":"12","key":"19_CR13","doi-asserted-by":"publisher","first-page":"4093","DOI":"10.1111\/cns.14298","volume":"29","author":"WR Kwapong","year":"2023","unstructured":"Kwapong, W.R., et al.: Retinal microvascular and structural changes in intracranial hypertension patients correlate with intracranial pressure. CNS Neurosci. Ther. 29(12), 4093\u20134101 (2023)","journal-title":"CNS Neurosci. Ther."},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Lee, Y.C., et al.: Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction. NPJ Digit. Med. 6(1), 14 (2023)","DOI":"10.1038\/s41746-023-00748-4"},{"issue":"3","key":"19_CR15","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1109\/JBHI.2021.3118104","volume":"26","author":"R Li","year":"2022","unstructured":"Li, R., et al.: 3D graph-connectivity constrained network for hepatic vessel segmentation. IEEE J. Biomed. Health Inform. 26(3), 1251\u20131262 (2022). https:\/\/doi.org\/10.1109\/JBHI.2021.3118104","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"25","key":"19_CR16","doi-asserted-by":"publisher","first-page":"2372","DOI":"10.1016\/j.jacc.2022.11.001","volume":"80","author":"M Lindstrom","year":"2022","unstructured":"Lindstrom, M., et al.: Global burden of cardiovascular diseases and risks collaboration, 1990\u20132021. J. Am. Coll. Cardiol. 80(25), 2372\u20132425 (2022)","journal-title":"J. Am. Coll. Cardiol."},{"issue":"9","key":"19_CR17","doi-asserted-by":"publisher","first-page":"2316","DOI":"10.1161\/STROKEAHA.123.044072","volume":"54","author":"Y Liu","year":"2023","unstructured":"Liu, Y., et al.: Functional outcome prediction in acute ischemic stroke using a fused imaging and clinical deep learning model. Stroke 54(9), 2316\u20132327 (2023)","journal-title":"Stroke"},{"issue":"6","key":"19_CR18","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1109\/MCG.2009.130","volume":"29","author":"J Meyer-Spradow","year":"2009","unstructured":"Meyer-Spradow, J., Ropinski, T., Mensmann, J., Hinrichs, K.: Voreen: a rapid-prototyping environment for ray-casting-based volume visualizations. IEEE Comput. Graphics Appl. 29(6), 6\u201313 (2009). https:\/\/doi.org\/10.1109\/MCG.2009.130","journal-title":"IEEE Comput. Graphics Appl."},{"key":"19_CR19","doi-asserted-by":"publisher","unstructured":"Mishra, S., Wang, Y.X., Wei, C., Chen, D., Hu, X.: VTG-Net: a CNN based vessel topology graph network for retinal artery\/vein classification. Front. Med. 8, 750396 (2021). https:\/\/doi.org\/10.3389\/fmed.2021.750396","DOI":"10.3389\/fmed.2021.750396"},{"issue":"3","key":"19_CR20","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":"19_CR21","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol.\u00a0139, pp. 8748\u20138763. PMLR (18\u201324 Jul 2021) (2021). https:\/\/proceedings.mlr.press\/v139\/radford21a.html"},{"issue":"1","key":"19_CR22","doi-asserted-by":"publisher","first-page":"2436","DOI":"10.1038\/s41467-023-38125-0","volume":"14","author":"A Radhakrishnan","year":"2023","unstructured":"Radhakrishnan, A., et al.: Cross-modal autoencoder framework learns holistic representations of cardiovascular state. Nat. Commun. 14(1), 2436 (2023)","journal-title":"Nat. Commun."},{"key":"19_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105781","volume":"90","author":"AJ Sheela","year":"2024","unstructured":"Sheela, A.J., Krishnamurthy, M.: Revolutionizing cardiovascular risk prediction: a novel image-based approach using fundus analysis and deep learning. Biomed. Signal Process. Control 90, 105781 (2024)","journal-title":"Biomed. Signal Process. Control"},{"key":"19_CR24","doi-asserted-by":"publisher","unstructured":"Shin, S.Y., Lee, S., Yun, I.D., Lee, K.M.: Deep vessel segmentation by learning graphical connectivity. Med. Image Anal. 58, 101556 (2019). https:\/\/doi.org\/10.1016\/j.media.2019.101556, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1361841519300982","DOI":"10.1016\/j.media.2019.101556"},{"issue":"3","key":"19_CR25","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1001779","volume":"12","author":"C Sudlow","year":"2015","unstructured":"Sudlow, C., et al.: UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12(3), e1001779 (2015)","journal-title":"PLoS Med."},{"key":"19_CR26","doi-asserted-by":"crossref","unstructured":"Tan, K., Marvell, Y.A., Gunawan, A.A.S.: Early ischemic stroke detection using deep learning: a systematic literature review. In: 2023 International Seminar on Application for Technology of Information and Communication (iSemantic), pp. 7\u201311. IEEE (2023)","DOI":"10.1109\/iSemantic59612.2023.10295339"},{"issue":"1","key":"19_CR27","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1186\/s12916-022-02684-8","volume":"21","author":"RMWW Tseng","year":"2023","unstructured":"Tseng, R.M.W.W., et al.: Validation of a deep-learning-based retinal biomarker (RETI-CVD) in the prediction of cardiovascular disease: data from uk biobank. BMC Med. 21(1), 28 (2023)","journal-title":"BMC Med."},{"key":"19_CR28","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018). https:\/\/openreview.net\/forum?id=rJXMpikCZ"},{"issue":"7","key":"19_CR29","doi-asserted-by":"publisher","first-page":"2393","DOI":"10.1161\/STROKEAHA.121.036204","volume":"53","author":"JM Wardlaw","year":"2022","unstructured":"Wardlaw, J.M., Mair, G., Von Kummer, R., Williams, M.C., Li, W., Storkey, A.J., Trucco, E., Liebeskind, D.S., Farrall, A., Bath, P.M., et al.: Accuracy of automated computer-aided diagnosis for stroke imaging: a critical evaluation of current evidence. Stroke 53(7), 2393\u20132403 (2022)","journal-title":"Stroke"},{"key":"19_CR30","unstructured":"Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., Leskovec, J.: Hierarchical graph representation learning with differentiable pooling. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol.\u00a031. Curran Associates, Inc. (2018), https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2018\/file\/e77dbaf6759253c7c6d0efc5690369c7-Paper.pdf"},{"key":"19_CR31","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/978-3-031-18814-5_4","volume-title":"Multiscale Multimodal Medical Imaging","author":"H Yu","year":"2022","unstructured":"Yu, H., Zhao, J., Zhang, L.: Vessel segmentation via link prediction of graph neural networks. In: Li, X., Lv, J., Huo, Y., Dong, B., Leahy, R.M., Li, Q. (eds.) Multiscale Multimodal Medical Imaging, pp. 34\u201343. Springer Nature Switzerland, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-18814-5_4"},{"issue":"7981","key":"19_CR32","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1038\/s41586-023-06555-x","volume":"622","author":"Y Zhou","year":"2023","unstructured":"Zhou, Y., et al.: A foundation model for generalizable disease detection from retinal images. Nature 622(7981), 156\u2013163 (2023)","journal-title":"Nature"},{"issue":"7","key":"19_CR33","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1167\/tvst.11.7.12","volume":"11","author":"Y Zhou","year":"2022","unstructured":"Zhou, Y., et al.: AutoMorph: automated retinal vascular morphology quantification via a deep learning pipeline. Transl. Vis. Sci. Technol. 11(7), 12\u201312 (2022)","journal-title":"Transl. Vis. Sci. Technol."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-84525-3_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T11:01:07Z","timestamp":1744628467000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-84525-3_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031845246","9783031845253"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-84525-3_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"13 April 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":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}