{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:26:53Z","timestamp":1785421613423,"version":"3.56.0"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031439926","type":"print"},{"value":"9783031439933","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43993-3_33","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:57Z","timestamp":1696115337000},"page":"338-347","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Dynamic Graph Neural Representation Based Multi-modal Fusion Model for\u00a0Cognitive Outcome Prediction in\u00a0Stroke Cases"],"prefix":"10.1007","author":[{"given":"Shuting","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baochang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Rueckert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Veronika A.","family":"Zimmer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"issue":"365","key":"33_CR1","first-page":"1","volume":"2","author":"BB Avants","year":"2009","unstructured":"Avants, B.B., Tustison, N., Song, G., et al.: Advanced normalization tools (ANTS). Insight j 2(365), 1\u201335 (2009)","journal-title":"Insight j"},{"issue":"6","key":"33_CR2","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1177\/17474930211045836","volume":"17","author":"EL Ball","year":"2022","unstructured":"Ball, E.L., et al.: Predicting post-stroke cognitive impairment using acute CT neuroimaging: a systematic review and meta-analysis. Int. J. Stroke 17(6), 618\u2013627 (2022)","journal-title":"Int. J. Stroke"},{"key":"33_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/978-3-031-16919-9_13","volume-title":"Predictive Intelligence in Medicine","author":"M Binzer","year":"2022","unstructured":"Binzer, M., Hammernik, K., Rueckert, D., Zimmer, V.A.: Long-term cognitive outcome prediction in stroke patients using multi-task learning on imaging and tabular data. In: Rekik, I., Adeli, E., Park, S.H., Cintas, C. (eds.) PRIME 2022. LNCS, vol. 13564, pp. 137\u2013148. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16919-9_13"},{"key":"33_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2019.105242","volume":"187","author":"MA Ebrahimighahnavieh","year":"2020","unstructured":"Ebrahimighahnavieh, M.A., Luo, S., Chiong, R.: Deep learning to detect Alzheimer\u2019s disease from neuroimaging: a systematic literature review. Comput. Methods Programs Biomed. 187, 105242 (2020)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"4","key":"33_CR5","doi-asserted-by":"publisher","first-page":"1152","DOI":"10.1002\/alz.12744","volume":"19","author":"MK Georgakis","year":"2023","unstructured":"Georgakis, M.K., et al.: Cerebral small vessel disease burden and cognitive and functional outcomes after stroke: a multicenter prospective cohort study. Alzheimer\u2019s Dementia 19(4), 1152\u20131163 (2023)","journal-title":"Alzheimer\u2019s Dementia"},{"issue":"7","key":"33_CR6","doi-asserted-by":"publisher","first-page":"1685","DOI":"10.1093\/cercor\/bhp232","volume":"20","author":"M Grothe","year":"2010","unstructured":"Grothe, M., et al.: Reduction of basal forebrain cholinergic system parallels cognitive impairment in patients at high risk of developing Alzheimer\u2019s disease. Cereb. Cortex 20(7), 1685\u20131695 (2010)","journal-title":"Cereb. Cortex"},{"issue":"7","key":"33_CR7","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0129211","volume":"10","author":"RA Heckemann","year":"2015","unstructured":"Heckemann, R.A., et al.: Brain extraction using label propagation and group agreement: pincram. PLoS ONE 10(7), e0129211 (2015)","journal-title":"PLoS ONE"},{"key":"33_CR8","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"issue":"1","key":"33_CR9","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.media.2014.12.003","volume":"21","author":"C Ledig","year":"2015","unstructured":"Ledig, C., et al.: Robust whole-brain segmentation: application to traumatic brain injury. Med. Image Anal. 21(1), 40\u201358 (2015)","journal-title":"Med. Image Anal."},{"issue":"3","key":"33_CR10","doi-asserted-by":"publisher","first-page":"297","DOI":"10.5853\/jos.2021.02376","volume":"23","author":"JS Lim","year":"2021","unstructured":"Lim, J.S., Lee, J.J., Woo, C.W.: Post-stroke cognitive impairment: pathophysiological insights into brain disconnectome from advanced neuroimaging analysis techniques. J. Stroke 23(3), 297\u2013311 (2021)","journal-title":"J. Stroke"},{"key":"33_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.116459","volume":"208","author":"M Liu","year":"2020","unstructured":"Liu, M., et al.: A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer\u2019s disease. Neuroimage 208, 116459 (2020)","journal-title":"Neuroimage"},{"key":"33_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1007\/978-3-030-87240-3_66","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"S P\u00f6lsterl","year":"2021","unstructured":"P\u00f6lsterl, S., Wolf, T.N., Wachinger, C.: Combining 3D image and tabular data via the dynamic affine feature map transform. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 688\u2013698. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87240-3_66"},{"issue":"2","key":"33_CR13","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1523\/JNEUROSCI.3617-14.2015","volume":"35","author":"NJ Ray","year":"2015","unstructured":"Ray, N.J., et al.: Cholinergic basal forebrain structure influences the reconfiguration of white matter connections to support residual memory in mild cognitive impairment. J. Neurosci. 35(2), 739\u2013747 (2015)","journal-title":"J. Neurosci."},{"key":"33_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2020.108669","volume":"337","author":"G Uysal","year":"2020","unstructured":"Uysal, G., Ozturk, M.: Hippocampal atrophy based Alzheimer\u2019s disease diagnosis via machine learning methods. J. Neurosci. Methods 337, 108669 (2020)","journal-title":"J. Neurosci. Methods"},{"key":"33_CR15","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"issue":"1","key":"33_CR16","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1177\/23969873211000258","volume":"6","author":"A Verdelho","year":"2021","unstructured":"Verdelho, A., et al.: Cognitive impairment in patients with cerebrovascular disease: a white paper from the links between stroke ESO dementia committee. Eur. Stroke J. 6(1), 5\u201317 (2021)","journal-title":"Eur. Stroke J."},{"issue":"6","key":"33_CR17","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1016\/S1474-4422(21)00060-0","volume":"20","author":"NA Weaver","year":"2021","unstructured":"Weaver, N.A., et al.: Strategic infarct locations for post-stroke cognitive impairment: a pooled analysis of individual patient data from 12 acute ischaemic stroke cohorts. Lancet Neurol. 20(6), 448\u2013459 (2021)","journal-title":"Lancet Neurol."},{"key":"33_CR18","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.ymeth.2022.04.015","volume":"204","author":"G Zheng","year":"2022","unstructured":"Zheng, G., et al.: A transformer-based multi-features fusion model for prediction of conversion in mild cognitive impairment. Methods 204, 241\u2013248 (2022)","journal-title":"Methods"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43993-3_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T16:09:31Z","timestamp":1712074171000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43993-3_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439926","9783031439933"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43993-3_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","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":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","order":11,"name":"conference_url","label":"Conference URL","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","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":"730","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":"32% - 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)"}}]}}