{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:03:33Z","timestamp":1760709813448,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030117221"},{"type":"electronic","value":"9783030117238"}],"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\/978-3-030-11723-8_6","type":"book-chapter","created":{"date-parts":[[2019,1,25]],"date-time":"2019-01-25T13:47:41Z","timestamp":1548424061000},"page":"57-69","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["CNN Prediction of Future Disease Activity for Multiple Sclerosis Patients from Baseline MRI and Lesion Labels"],"prefix":"10.1007","author":[{"given":"Nazanin Mohammadi","family":"Sepahvand","sequence":"first","affiliation":[]},{"given":"Tal","family":"Hassner","sequence":"additional","affiliation":[]},{"given":"Douglas L.","family":"Arnold","sequence":"additional","affiliation":[]},{"given":"Tal","family":"Arbel","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,1,26]]},"reference":[{"issue":"11","key":"6_CR1","doi-asserted-by":"publisher","first-page":"2059","DOI":"10.1093\/brain\/120.11.2059","volume":"120","author":"F Barkhof","year":"1997","unstructured":"Barkhof, F., et al.: Comparison of MRI criteria at first presentation to predict conversion to clinically definite multiple sclerosis. Brain 120(11), 2059\u20132069 (1997)","journal-title":"Brain"},{"issue":"8","key":"6_CR2","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. TPAMI 35(8), 1798\u20131828 (2013)","journal-title":"TPAMI"},{"key":"6_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1007\/978-3-319-10470-6_58","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2014","author":"T Brosch","year":"2014","unstructured":"Brosch, T., Yoo, Y., Li, D.K.B., Traboulsee, A., Tam, R.: Modeling the variability in brain morphology and lesion distribution in multiple sclerosis by deep learning. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8674, pp. 462\u2013469. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10470-6_58"},{"key":"6_CR4","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.neuroimage.2016.12.064","volume":"148","author":"A Carass","year":"2017","unstructured":"Carass, A., et al.: Longitudinal multiple sclerosis lesion segmentation: resource and challenge. Neuroimage 148, 77\u2013102 (2017)","journal-title":"Neuroimage"},{"key":"6_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1007\/978-3-319-66179-7_22","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2017","author":"A Doyle","year":"2017","unstructured":"Doyle, A., Precup, D., Arnold, D.L., Arbel, T.: Predicting future disease activity and treatment responders for multiple sclerosis patients using a bag-of-lesions brain representation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 186\u2013194. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_22"},{"issue":"8","key":"6_CR6","first-page":"1490","volume":"32","author":"C Elliott","year":"2013","unstructured":"Elliott, C., et al.: Temporally consistent probabilistic detection of new multiple sclerosis lesions in brain MRI. IEEE TMI 32(8), 1490\u20131503 (2013)","journal-title":"IEEE TMI"},{"issue":"12","key":"6_CR7","doi-asserted-by":"publisher","first-page":"1098","DOI":"10.1056\/NEJMoa1114287","volume":"367","author":"R Gold","year":"2012","unstructured":"Gold, R., et al.: Placebo-controlled phase 3 study of oral BG-12 for relapsing multiple sclerosis. N. Engl. J. Med. 367(12), 1098\u20131107 (2012)","journal-title":"N. Engl. J. Med."},{"key":"6_CR8","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)"},{"key":"6_CR9","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML, pp. 448\u2013456 (2015)"},{"issue":"6","key":"6_CR10","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1177\/1756285617708911","volume":"10","author":"U Kaunzner","year":"2017","unstructured":"Kaunzner, U., Gauthier, S.: MRI in the assessment and monitoring of multiple sclerosis: an update on best practice. Ther. Adv. Neurol. Disord. 10(6), 247\u2013261 (2017)","journal-title":"Ther. Adv. Neurol. Disord."},{"key":"6_CR11","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2014)"},{"key":"6_CR12","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1097\u20131105 (2012)"},{"issue":"10","key":"6_CR13","first-page":"1993","volume":"34","author":"B Menze","year":"2015","unstructured":"Menze, B., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE TMI 34(10), 1993 (2015)","journal-title":"IEEE TMI"},{"issue":"12","key":"6_CR14","doi-asserted-by":"publisher","first-page":"1614","DOI":"10.1177\/1352458517729456","volume":"23","author":"M Moccia","year":"2017","unstructured":"Moccia, M., de Stefano, N., Barkhof, F.: Imaging outcome measures for progressive multiple sclerosis trials. Mult. Scler. J. 23(12), 1614\u20131626 (2017)","journal-title":"Mult. Scler. J."},{"issue":"6","key":"6_CR15","doi-asserted-by":"publisher","first-page":"1072","DOI":"10.1002\/(SICI)1522-2594(199912)42:6<1072::AID-MRM11>3.0.CO;2-M","volume":"42","author":"L Ny\u00fal","year":"1999","unstructured":"Ny\u00fal, L., Udupa, J.: On standardizing the MR image intensity scale. Magn. Reson. Med.: Off. J. Int. Soc. Magn. Reson. Med. 42(6), 1072\u20131081 (1999)","journal-title":"Magn. Reson. Med.: Off. J. Int. Soc. Magn. Reson. Med."},{"issue":"2","key":"6_CR16","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.nic.2017.01.001","volume":"27","author":"J R\u00edo","year":"2017","unstructured":"R\u00edo, J., et al.: MR imaging in monitoring and predicting treatment response in multiple sclerosis. Neuroimaging Clin. 27(2), 277\u2013287 (2017)","journal-title":"Neuroimaging Clin."},{"key":"6_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"1","key":"6_CR18","first-page":"87","volume":"17","author":"J Sled","year":"1998","unstructured":"Sled, J., Zijdenbos, A., Evans, A.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE TMI 17(1), 87\u201397 (1998)","journal-title":"IEEE TMI"},{"issue":"3","key":"6_CR19","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1002\/hbm.10062","volume":"17","author":"S Smith","year":"2002","unstructured":"Smith, S.: Fast robust automated brain extraction. Hum. Brain Mapp. 17(3), 143\u2013155 (2002)","journal-title":"Hum. Brain Mapp."},{"issue":"7","key":"6_CR20","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1016\/S1474-4422(13)70103-0","volume":"12","author":"MP Sormani","year":"2013","unstructured":"Sormani, M.P., Bruzzi, P.: MRI lesions as a surrogate for relapses in multiple sclerosis: a meta-analysis of randomised trials. Lancet Neurol. 12(7), 669\u2013676 (2013)","journal-title":"Lancet Neurol."},{"issue":"1","key":"6_CR21","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1177\/1756285614560733","volume":"8","author":"M Stangel","year":"2015","unstructured":"Stangel, M., et al.: Towards the implementation of\u00a0\u2018no evidence of disease activity\u2019 in multiple sclerosis treatment: the multiple sclerosis decision model. Ther. Adv. Neurol. Disord. 8(1), 3\u201313 (2015)","journal-title":"Ther. Adv. Neurol. Disord."},{"issue":"1","key":"6_CR22","doi-asserted-by":"publisher","first-page":"22","DOI":"10.7326\/M14-2057","volume":"163","author":"B Windham","year":"2015","unstructured":"Windham, B., et al.: Small brain lesions and incident stroke and mortality: a cohort study. Ann. Intern. Med. 163(1), 22\u201331 (2015)","journal-title":"Ann. Intern. Med."},{"key":"6_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1007\/978-3-319-46976-8_10","volume-title":"Deep Learning and Data Labeling for Medical Applications","author":"Y Yoo","year":"2016","unstructured":"Yoo, Y., et al.: Deep learning of brain lesion patterns for predicting future disease activity in patients with early symptoms of multiple sclerosis. In: Carneiro, G., et al. (eds.) LABELS\/DLMIA-2016. LNCS, vol. 10008, pp. 86\u201394. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46976-8_10"}],"container-title":["Lecture Notes in Computer Science","Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-11723-8_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T15:57:00Z","timestamp":1710345420000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-11723-8_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030117221","9783030117238"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-11723-8_6","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":"26 January 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BrainLes","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International MICCAI Brainlesion Workshop","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Granada","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwb2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.brainlesion-workshop.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"95","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":"92","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":"97% - 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":"3","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"}]}}