{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:44:35Z","timestamp":1759333475279,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030322441"},{"type":"electronic","value":"9783030322458"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-32245-8_88","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T23:08:49Z","timestamp":1570662529000},"page":"796-805","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Learning-Guided Infinite Network Atlas Selection for Predicting Longitudinal Brain Network Evolution from a Single Observation"],"prefix":"10.1007","author":[{"given":"Baha Eddine","family":"Ezzine","sequence":"first","affiliation":[]},{"given":"Islem","family":"Rekik","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"key":"88_CR1","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/j.neuroimage.2017.03.012","volume":"152","author":"I Rekik","year":"2017","unstructured":"Rekik, I., Li, G., Yap, P., Chen, G., Lin, W., Shen, D.: Joint prediction of longitudinal development of cortical surfaces and white matter fibers from neonatal MRI. NeuroImage 152, 411\u2013424 (2017)","journal-title":"NeuroImage"},{"doi-asserted-by":"crossref","unstructured":"Gafuro\u011flu, C., Rekik, I., et al.: Joint prediction and classification of brain image evolution trajectories from baseline brain image with application to early dementia. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 437\u2013445 (2018)","key":"88_CR2","DOI":"10.1007\/978-3-030-00931-1_50"},{"unstructured":"Soussia, M., Rekik, I.: A review on image-and network-based brain data analysis techniques for Alzheimer\u2019s disease diagnosis reveals a gap in developing predictive methods for prognosis. arXiv preprint arXiv:1808.01951 (2018)","key":"88_CR3"},{"key":"88_CR4","doi-asserted-by":"publisher","first-page":"766","DOI":"10.1016\/j.neuroimage.2010.06.013","volume":"56","author":"R Cuingnet","year":"2011","unstructured":"Cuingnet, R., et al.: Automatic classification of patients with Alzheimer\u2019s disease from structural MRI: a comparison of ten methods using the adni database. NeuroImage 56, 766\u2013781 (2011)","journal-title":"NeuroImage"},{"key":"88_CR5","doi-asserted-by":"publisher","first-page":"1305","DOI":"10.1002\/hbm.22254","volume":"35","author":"M Liu","year":"2014","unstructured":"Liu, M., Zhang, D., Shen, D., Initiative, A.D.N.: Hierarchical fusion of features and classifier decisions for Alzheimer\u2019s disease diagnosis. Hum. Brain Mapp. 35, 1305\u20131319 (2014)","journal-title":"Hum. Brain Mapp."},{"key":"88_CR6","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1016\/j.neuroimage.2011.03.029","volume":"56","author":"J Koikkalainen","year":"2011","unstructured":"Koikkalainen, J., et al.: Multi-template tensor-based morphometry: application to analysis of Alzheimer\u2019s disease. NeuroImage 56, 1134\u20131144 (2011)","journal-title":"NeuroImage"},{"key":"88_CR7","doi-asserted-by":"publisher","first-page":"5052","DOI":"10.1002\/hbm.22531","volume":"35","author":"R Min","year":"2014","unstructured":"Min, R., Wu, G., Cheng, J., Wang, Q., Shen, D., Alzheimer\u2019s Disease Neuroimaging Initiative: Multi-atlas based representations for Alzheimer\u2019s disease diagnosis. Hum. Brain Mapp. 35, 5052\u20135070 (2014)","journal-title":"Hum. Brain Mapp."},{"key":"88_CR8","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.media.2018.10.012","volume":"51","author":"L Fang","year":"2018","unstructured":"Fang, L., et al.: Automatic brain labeling via multi-atlas guided fully convolutional networks. Med. Image Anal. 51, 157\u2013168 (2018)","journal-title":"Med. Image Anal."},{"doi-asserted-by":"crossref","unstructured":"Roffo, G., Melzi, S., Cristani, M.: Infinite feature selection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4202\u20134210 (2015)","key":"88_CR9","DOI":"10.1109\/ICCV.2015.478"},{"key":"88_CR10","first-page":"869","volume":"70","author":"B Wang","year":"2017","unstructured":"Wang, B., Zhu, J., Pierson, E., Ramazzotti, D., Batzoglou, S.: Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. Nature 70, 869\u201379 (2017)","journal-title":"Nature"},{"key":"88_CR11","doi-asserted-by":"publisher","first-page":"4103","DOI":"10.1038\/s41598-018-21568-7","volume":"8","author":"I Mahjoub","year":"2018","unstructured":"Mahjoub, I., Mahjoub, M.A., Rekik, I.: Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states. Sci. Rep. 8, 4103 (2018)","journal-title":"Sci. Rep."},{"key":"88_CR12","doi-asserted-by":"publisher","first-page":"70","DOI":"10.3389\/fninf.2018.00070","volume":"12","author":"M Soussia","year":"2018","unstructured":"Soussia, M., Rekik, I.: Unsupervised manifold learning using high-order morphological brain networks derived from T1-w MRI for autism diagnosis. Front. Neuroinform. 12, 70 (2018)","journal-title":"Front. Neuroinform."},{"key":"88_CR13","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1089\/brain.2018.0578","volume":"9","author":"A Lisowska","year":"2019","unstructured":"Lisowska, A., Rekik, I.: ADNI: joint pairing and structured mapping of convolutional brain morphological multiplexes for early dementia diagnosis. Brain Connect. 9, 22\u201336 (2019)","journal-title":"Brain Connect."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32245-8_88","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:04:11Z","timestamp":1728518651000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32245-8_88"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322441","9783030322458"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32245-8_88","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"10 October 2019","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":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2019.org\/","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":"1730","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":"539","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":"31% - 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":"3.07","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":"6.31","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}