{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T16:03:14Z","timestamp":1790179394440,"version":"4.1.0"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032381712","type":"print"},{"value":"9783032381729","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"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":[[2027]]},"DOI":"10.1007\/978-3-032-38172-9_54","type":"book-chapter","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T15:17:22Z","timestamp":1790176642000},"page":"567-577","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["StrokeTimer: Robust Representation Learning for\u00a0Ischemic Stroke Onset-Time Estimation from\u00a0Non-Contrast CT"],"prefix":"10.1007","author":[{"given":"Weiru","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susanne G. H.","family":"Olthuis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizaveta","family":"Lavrova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert J.","family":"van Oostenbrugge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charles B. L. M.","family":"Majoie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wim H.","family":"van Zwam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruisheng","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"54_CR1","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.neucom.2018.12.085","volume":"392","author":"Z Akkus","year":"2020","unstructured":"Akkus, Z., Kostandy, P., Philbrick, K.A., Erickson, B.J.: Robust brain extraction tool for CT head images. Neurocomputing 392, 189\u2013195 (2020)","journal-title":"Neurocomputing"},{"issue":"8","key":"54_CR2","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1056\/NEJMoa1713973","volume":"378","author":"GW Albers","year":"2018","unstructured":"Albers, G.W., et al.: Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging. N. Engl. J. Med. 378(8), 708\u2013718 (2018)","journal-title":"N. Engl. J. Med."},{"key":"54_CR3","doi-asserted-by":"crossref","unstructured":"Chattopadhyay, A., Sarkar, A., Howlader, P., Balasubramanian, V.N.: Grad-cam++: generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 839\u2013847. IEEE (2018)","DOI":"10.1109\/WACV.2018.00097"},{"key":"54_CR4","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020)"},{"issue":"10","key":"54_CR5","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/S1474-4422(21)00252-0","volume":"20","author":"GS Collaborators","year":"2021","unstructured":"Collaborators, G.S., et al.: Global, regional, and national burden of stroke and its risk factors, 1990\u20132019: a systematic analysis for the global burden of disease study 2019. Lancet. Neurol. 20(10), 795 (2021)","journal-title":"Lancet. Neurol."},{"key":"54_CR6","doi-asserted-by":"crossref","unstructured":"Diederik, P.K., Max, W.: An introduction to variational autoencoders. Found. Trends\u00ae Mach. Learn. 12(4), 307\u2013392 (2019)","DOI":"10.1561\/2200000056"},{"key":"54_CR7","doi-asserted-by":"crossref","unstructured":"Feigin, V.L., et al.: World stroke organization: global stroke fact sheet 2025. Int. J. Stroke 20(2), 132\u2013144 (2025)","DOI":"10.1177\/17474930241308142"},{"key":"54_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000\u201316009 (2022)","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"54_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"54_CR10","doi-asserted-by":"crossref","unstructured":"Jansen, I.G., Mulder, M.J., Goldhoorn, R.J.B.: Endovascular treatment for acute ischaemic stroke in routine clinical practice: prospective, observational cohort study (MR clean registry). BMJ 360 (2018)","DOI":"10.1136\/bmj.k949"},{"key":"54_CR11","doi-asserted-by":"crossref","unstructured":"Jiang, L., et al.: Diffusion-\/perfusion-weighted imaging fusion to automatically identify stroke within 4.5 h. European Radiol. 34(10), 6808\u20136819 (2024)","DOI":"10.1007\/s00330-024-10619-5"},{"issue":"6","key":"54_CR12","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1038\/s41551-021-00733-w","volume":"5","author":"MK Kanakasabapathy","year":"2021","unstructured":"Kanakasabapathy, M.K., et al.: Adaptive adversarial neural networks for the analysis of lossy and domain-shifted datasets of medical images. Nat. Biomed. Eng. 5(6), 571\u2013585 (2021)","journal-title":"Nat. Biomed. Eng."},{"key":"54_CR13","unstructured":"Kang, B., et al.: Decoupling representation and classifier for long-tailed recognition. In: International Conference on Learning Representations (2020)"},{"issue":"7","key":"54_CR14","doi-asserted-by":"publisher","first-page":"1786","DOI":"10.1161\/01.STR.0000019125.80118.99","volume":"33","author":"T Kucinski","year":"2002","unstructured":"Kucinski, T., et al.: Correlation of apparent diffusion coefficient and computed tomography density in acute ischemic stroke. Stroke 33(7), 1786\u20131791 (2002)","journal-title":"Stroke"},{"key":"54_CR15","unstructured":"Li, J., Zhou, P., Xiong, C., Hoi, S.: Prototypical contrastive learning of unsupervised representations. In: International Conference on Learning Representations (2021)"},{"issue":"2","key":"54_CR16","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"TY Lin","year":"2020","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 318\u2013327 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"54_CR17","doi-asserted-by":"crossref","unstructured":"Marcus, A., et al.: Deep learning biomarker of chronometric and biological ischemic stroke lesion age from unenhanced ct. npj Digit. Med. 7(1), 338 (2024)","DOI":"10.1038\/s41746-024-01325-z"},{"key":"54_CR18","doi-asserted-by":"crossref","unstructured":"Perez, E., Strub, F., De Vries, H., Dumoulin, V., Courville, A.: Film: visual reasoning with a general conditioning layer. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11671"},{"key":"54_CR19","doi-asserted-by":"crossref","unstructured":"van Poppel, L.M., et\u00a0al.: Machine learning models for CT-based classification of ischemic stroke onset time within or beyond 4.5 h: a comparison of approaches. European Radiol., 1\u201311 (2025)","DOI":"10.1007\/s00330-025-12189-6"},{"issue":"2","key":"54_CR20","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1148\/radiol.2292020402","volume":"229","author":"JM Provenzale","year":"2003","unstructured":"Provenzale, J.M., Jahan, R., Naidich, T.P., Fox, A.J.: Assessment of the patient with hyperacute stroke: imaging and therapy. Radiology 229(2), 347\u2013359 (2003)","journal-title":"Radiology"},{"key":"54_CR21","first-page":"4175","volume":"33","author":"J Ren","year":"2020","unstructured":"Ren, J., Yu, C., Ma, X., Zhao, H., Yi, S., et al.: Balanced meta-softmax for long-tailed visual recognition. Adv. Neural. Inf. Process. Syst. 33, 4175\u20134186 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"54_CR22","doi-asserted-by":"crossref","unstructured":"Sarhan, M.H., Navab, N., Eslami, A., Albarqouni, S.: Fairness by learning orthogonal disentangled representations. In: European Conference on Computer Vision, pp. 746\u2013761. Springer (2020)","DOI":"10.1007\/978-3-030-58526-6_44"},{"key":"54_CR23","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, p. 30 (2017)"},{"key":"54_CR24","doi-asserted-by":"crossref","unstructured":"Sun, J., et al.: Multi-grained contrastive representation learning for label-efficient lesion segmentation and onset time classification of acute ischemic stroke. Med. Image Anal. 97, 103250 (2024)","DOI":"10.1016\/j.media.2024.103250"},{"issue":"9","key":"54_CR25","doi-asserted-by":"publisher","first-page":"2707","DOI":"10.1161\/STROKEAHA.115.009701","volume":"46","author":"G Thomalla","year":"2015","unstructured":"Thomalla, G., Gerloff, C.: Treatment concepts for wake-up stroke and stroke with unknown time of symptom onset. Stroke 46(9), 2707\u20132713 (2015)","journal-title":"Stroke"},{"key":"54_CR26","doi-asserted-by":"crossref","unstructured":"Vorberg, L., Rist, L., Ditt, H., Maier, A., Taubmann, O.: Leveraging last-known-well times for radiomics-based stroke onset estimation from non-contrast ct. In: Stroke Workshop on Imaging and Treatment Challenges, pp. 52\u201361. Springer (2025)","DOI":"10.1007\/978-3-032-07945-9_6"},{"key":"54_CR27","doi-asserted-by":"crossref","unstructured":"Wang, S., et al.: Generative artificial intelligence in medical imaging: foundations, progress, and clinical translation. Research 8, 1029 (2025)","DOI":"10.34133\/research.1029"},{"key":"54_CR28","first-page":"1","volume":"62","author":"Y Wang","year":"2024","unstructured":"Wang, Y., Albrecht, C.M., Zhu, X.X.: Multilabel-guided soft contrastive learning for efficient earth observation pretraining. IEEE Trans. Geosci. Remote Sens. 62, 1\u201316 (2024)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"54_CR29","unstructured":"Wang, Y., Zhang, B., Hou, W., Wu, Z., Wang, J., Shinozaki, T.: Margin calibration for long-tailed visual recognition. In: Asian Conference on Machine Learning, pp. 1101\u20131116. PMLR (2023)"},{"key":"54_CR30","doi-asserted-by":"crossref","unstructured":"Wu, L., Zhuang, J., Chen, H.: Voco: a simple-yet-effective volume contrastive learning framework for 3d medical image analysis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 22873\u201322882 (2024)","DOI":"10.1109\/CVPR52733.2024.02158"},{"key":"54_CR31","doi-asserted-by":"crossref","unstructured":"Yan, W., et al.: The domain shift problem of medical image segmentation and vendor-adaptation by UNET-GAN. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 623\u2013631. Springer (2019)","DOI":"10.1007\/978-3-030-32245-8_69"},{"key":"54_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, P., Wu, M.: Multi-label supervised contrastive learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 16786\u201316793 (2024)","DOI":"10.1609\/aaai.v38i15.29619"},{"key":"54_CR33","unstructured":"Zhao, H., Zhang, S., Wu, G., Moura, J.M., Costeira, J.P., Gordon, G.J.: Adversarial multiple source domain adaptation. In: Advances in Neural Information Processing Systems, p. 31 (2018)"},{"key":"54_CR34","doi-asserted-by":"crossref","unstructured":"Zuo, L., et al.: Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory. Neuroimage 243, 118569 (2021)","DOI":"10.1016\/j.neuroimage.2021.118569"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-38172-9_54","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T15:17:33Z","timestamp":1790176653000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-38172-9_54"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,24]]},"ISBN":["9783032381712","9783032381729"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-38172-9_54","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,24]]},"assertion":[{"value":"24 September 2026","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","label":"Disclosure of Interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"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":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","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":"27 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2026\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}