{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T10:40:54Z","timestamp":1761129654443,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030322502"},{"type":"electronic","value":"9783030322519"}],"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\/978-3-030-32251-9_68","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T23:08:49Z","timestamp":1570662529000},"page":"620-628","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Dynamic Routing Capsule Networks for Mild Cognitive Impairment Diagnosis"],"prefix":"10.1007","author":[{"given":"Zhicheng","family":"Jiao","sequence":"first","affiliation":[]},{"given":"Pu","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Tae-Eui","family":"Kam","sequence":"additional","affiliation":[]},{"given":"Li-Ming","family":"Hsu","sequence":"additional","affiliation":[]},{"given":"Ye","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Han","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"issue":"3","key":"68_CR1","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/j.jalz.2018.02.001","volume":"14","author":"Alzheimer\u2019s Association","year":"2018","unstructured":"Alzheimer\u2019s Association: 2018 Alzheimer\u2019s disease facts and figures. Alzheimer\u2019s Dement. 14(3), 367\u2013429 (2018)","journal-title":"Alzheimer\u2019s Dement."},{"issue":"9","key":"68_CR2","doi-asserted-by":"publisher","first-page":"3282","DOI":"10.1002\/hbm.23240","volume":"37","author":"X Chen","year":"2016","unstructured":"Chen, X., et al.: High-order resting-state functional connectivity network for MCI classification. Hum. Brain Mapp. 37(9), 3282\u20133296 (2016)","journal-title":"Hum. Brain Mapp."},{"key":"68_CR3","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1016\/j.neuroimage.2015.02.037","volume":"112","author":"E Challis","year":"2015","unstructured":"Challis, E., et al.: Gaussian process classification of Alzheimer\u2019s disease and mild cognitive impairment from resting-state fMRI. NeuroImage 112, 232\u2013243 (2015)","journal-title":"NeuroImage"},{"key":"68_CR4","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","volume":"19","author":"D Shen","year":"2017","unstructured":"Shen, D., et al.: Deep learning in medical image analysis. Ann. Rev. Biomed. Eng. 19, 221\u2013248 (2017)","journal-title":"Ann. Rev. Biomed. Eng."},{"key":"68_CR5","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1016\/j.neuroimage.2014.06.077","volume":"101","author":"H-I Suk","year":"2014","unstructured":"Suk, H.-I., et al.: Hierarchical feature representation and multimodal fusion with deep learning for AD\/MCI diagnosis. NeuroImage 101, 569\u2013582 (2014)","journal-title":"NeuroImage"},{"key":"68_CR6","doi-asserted-by":"crossref","unstructured":"Liu, S., et al.: Early diagnosis of Alzheimer\u2019s disease with deep learning. In: ISBI. IEEE (2014)","DOI":"10.1109\/ISBI.2014.6868045"},{"key":"68_CR7","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"},{"key":"68_CR8","unstructured":"Sabour, S., et al.: Dynamic routing between capsules. In: NeurIPS (2017)"},{"key":"68_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1007\/978-3-030-00934-2_82","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"A Mobiny","year":"2018","unstructured":"Mobiny, A., Van Nguyen, H.: Fast CapsNet for lung cancer screening. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 741\u2013749. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_82"},{"key":"68_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1007\/978-3-030-00934-2_44","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"A Pal","year":"2018","unstructured":"Pal, A., Chaturvedi, A., Garain, U., Chandra, A., Chatterjee, R., Senapati, S.: CapsDeMM: capsule network for detection of munro\u2019s microabscess in skin biopsy images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 389\u2013397. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_44"},{"key":"68_CR11","doi-asserted-by":"crossref","unstructured":"Afshar, P., et al.: Brain tumor type classification via capsule networks. In: ICIP. IEEE (2018)","DOI":"10.1109\/ICIP.2018.8451379"},{"issue":"1","key":"68_CR12","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1006\/nimg.2001.0978","volume":"15","author":"N Tzourio-Mazoyer","year":"2002","unstructured":"Tzourio-Mazoyer, N., et al.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1), 273\u2013289 (2002)","journal-title":"Neuroimage"},{"key":"68_CR13","unstructured":"Paszke, A., et al.: Automatic differentiation in PyTorch (2017)"}],"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\/978-3-030-32251-9_68","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:19:58Z","timestamp":1728519598000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32251-9_68"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322502","9783030322519"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32251-9_68","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"}]}}