{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T15:42:57Z","timestamp":1743003777922,"version":"3.40.3"},"publisher-location":"Cham","reference-count":8,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030326913"},{"type":"electronic","value":"9783030326920"}],"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\/978-3-030-32692-0_58","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T12:04:21Z","timestamp":1570622661000},"page":"507-515","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Adaptive Functional Connectivity Network Using Parallel Hierarchical BiLSTM for MCI Diagnosis"],"prefix":"10.1007","author":[{"given":"Yiqiao","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huifang","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chong-Yaw","family":"Wee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"key":"58_CR1","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.1109\/TMI.2018.2882189","volume":"38","author":"Y Li","year":"2018","unstructured":"Li, Y., et al.: Novel effective connectivity inference using ultra-group constrained orthogonal forward regression and elastic multilayer perceptron classifier for MCI identification. IEEE Trans. Med. Imaging 38, 1227\u20131239 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"58_CR2","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.neuron.2014.10.015","volume":"84","author":"VD Calhoun","year":"2014","unstructured":"Calhoun, V.D.: The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron 84(2), 262\u2013274 (2014)","journal-title":"Neuron"},{"key":"58_CR3","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.neuroimage.2016.12.061","volume":"160","author":"MG Preti","year":"2017","unstructured":"Preti, M.G.: The dynamic functional connectome: state-of-the-art and perspectives. Neuroimage 160, 41\u201354 (2017)","journal-title":"Neuroimage"},{"issue":"10","key":"58_CR4","doi-asserted-by":"publisher","first-page":"4719","DOI":"10.1093\/cercor\/bhw265","volume":"27","author":"TO Laumann","year":"2017","unstructured":"Laumann, T.O., et al.: On the stability of BOLD fMRI correlations. Cerebral Cortex 27(10), 4719\u20134732 (2017). https:\/\/doi.org\/10.1093\/cercor\/bhw265","journal-title":"Cerebral Cortex"},{"key":"58_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/978-3-030-00931-1_29","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"W Yan","year":"2018","unstructured":"Yan, W., Zhang, H., Sui, J., Shen, D.: Deep chronnectome learning via full bidirectional long short-term memory networks for MCI diagnosis. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 249\u2013257. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00931-1_29"},{"issue":"2","key":"58_CR6","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1007\/s00429-013-0524-8","volume":"219","author":"CY Wee","year":"2014","unstructured":"Wee, C.Y.: Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification. Brain Struct. Funct. 219(2), 641\u2013656 (2014)","journal-title":"Brain Struct. Funct."},{"key":"58_CR7","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.media.2018.11.006","volume":"52","author":"Y Li","year":"2019","unstructured":"Li, Y.: Multimodal hyper-connectivity of functional networks using functionally-weighted LASSO for MCI classification. Med. Image Anal. 52, 80\u201396 (2019)","journal-title":"Med. Image Anal."},{"key":"58_CR8","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.neucom.2018.09.076","volume":"323","author":"A Elsheikh","year":"2018","unstructured":"Elsheikh, A., et al.: Bidirectional handshaking LSTM for remaining useful life prediction. Neurocomputing 323, 148\u2013156 (2018)","journal-title":"Neurocomputing"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32692-0_58","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:04:52Z","timestamp":1728432292000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32692-0_58"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030326913","9783030326920"],"references-count":8,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32692-0_58","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":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","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":"13 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/mlmi2019.web.unc.edu\/","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":"158","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":"78","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":"49% - 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":"2","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":"4","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":"No","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"}]}}