{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:52:29Z","timestamp":1742950349870,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319661780"},{"type":"electronic","value":"9783319661797"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-66179-7_55","type":"book-chapter","created":{"date-parts":[[2017,9,3]],"date-time":"2017-09-03T19:24:46Z","timestamp":1504466686000},"page":"480-488","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Hierarchical Multimodal Fusion of Deep-Learned Lesion and Tissue Integrity Features in Brain MRIs for Distinguishing Neuromyelitis Optica from Multiple Sclerosis"],"prefix":"10.1007","author":[{"given":"Youngjin","family":"Yoo","sequence":"first","affiliation":[]},{"given":"Lisa Y. W.","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Su-Hyun","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Ho Jin","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Lisa Eunyoung","family":"Lee","sequence":"additional","affiliation":[]},{"given":"David K. B.","family":"Li","sequence":"additional","affiliation":[]},{"given":"Shannon","family":"Kolind","sequence":"additional","affiliation":[]},{"given":"Anthony","family":"Traboulsee","sequence":"additional","affiliation":[]},{"given":"Roger","family":"Tam","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,9,4]]},"reference":[{"issue":"11","key":"55_CR1","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.1212\/WNL.0000000000001367","volume":"84","author":"HJ Kim","year":"2015","unstructured":"Kim, H.J., Paul, F., Lana-Peixoto, M.A., et al.: MRI characteristics of neuromyelitis optica spectrum disorder: an international update. Neurology 84(11), 1165\u20131173 (2015)","journal-title":"Neurology"},{"key":"55_CR2","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.nicl.2015.01.001","volume":"7","author":"A Eshaghi","year":"2015","unstructured":"Eshaghi, A., Riyahi-Alam, S., Saeedi, R., et al.: Classification algorithms with multi-modal data fusion could accurately distinguish neuromyelitis optica from multiple sclerosis. NeuroImage Clin. 7, 306\u2013314 (2015)","journal-title":"NeuroImage Clin."},{"issue":"23","key":"55_CR3","doi-asserted-by":"publisher","first-page":"2463","DOI":"10.1212\/WNL.0000000000003395","volume":"87","author":"A Eshaghi","year":"2016","unstructured":"Eshaghi, A., Wottschel, V., et al.: Gray matter MRI differentiates neuromyelitis optica from multiple sclerosis using random forest. Neurology 87(23), 2463\u20132470 (2016)","journal-title":"Neurology"},{"key":"55_CR4","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., Tang, L.W., Brosch, T., Li, D.K.B., Metz, L., Traboulsee, A., Tam, R.: 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). doi:10.1007\/978-3-319-46976-8_10"},{"key":"55_CR5","doi-asserted-by":"crossref","unstructured":"Karpathy, A., Toderici, G., Shetty, S., et al.: Large-scale video classification with convolutional neural networks. In: Proceeding of IEEE CVPR (2014)","DOI":"10.1109\/CVPR.2014.223"},{"key":"55_CR6","unstructured":"Ngiam, J., Khosla, A., et al.: Multimodal deep learning. In: Proceeding of ICML (2011)"},{"issue":"2","key":"55_CR7","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1002\/ima.20277","volume":"21","author":"S Jeon","year":"2011","unstructured":"Jeon, S., Yoon, U., Park, J.S., et al.: Fully automated pipeline for quantification and localization of white matter hyperintensity in brain magnetic resonance image. International Journal of Imaging Systems and Technology 21(2), 193\u2013200 (2011)","journal-title":"International Journal of Imaging Systems and Technology"},{"issue":"2","key":"55_CR8","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1016\/j.neuroimage.2011.09.015","volume":"62","author":"M Jenkinson","year":"2012","unstructured":"Jenkinson, M., Beckmann, C.F., et al.: FSL. NeuroImage 62(2), 782\u2013790 (2012)","journal-title":"NeuroImage"},{"issue":"10","key":"55_CR9","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1145\/2001269.2001295","volume":"54","author":"H Lee","year":"2011","unstructured":"Lee, H., Grosse, R., Ranganath, R., et al.: Unsupervised learning of hierarchical representations with convolutional deep belief networks. Communications of the ACM 54(10), 95\u2013103 (2011)","journal-title":"Communications of the ACM"},{"key":"55_CR10","unstructured":"Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of ICML (2013)"},{"key":"55_CR11","unstructured":"Zeiler, M.: ADADELTA: An adaptive learning rate method. arXiv preprint arXiv:1212.5701 (2012)"},{"issue":"1","key":"55_CR12","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G.E., Krizhevsky, A., et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"55_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/3-540-49430-8_3","volume-title":"Neural Networks: Tricks of the Trade","author":"L Prechelt","year":"1998","unstructured":"Prechelt, L.: Early stopping - but when? In: Orr, G.B., M\u00fcller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 1524, pp. 55\u201369. Springer, Heidelberg (1998). doi:10.1007\/3-540-49430-8_3"},{"key":"55_CR14","series-title":"Lecture Notes in Computer Science","volume-title":"Neural Networks: Tricks of the Trade","year":"2012","unstructured":"Montavon, G., Orr, G.B., M\u00fcller, K.-R. (eds.): Neural Networks: Tricks of the Trade. LNCS, vol. 7700. Springer, Heidelberg (2012)"},{"key":"55_CR15","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1016\/j.neuroimage.2014.06.077","volume":"101","author":"HI Suk","year":"2014","unstructured":"Suk, H.I., Lee, S.W., et al.: Hierarchical feature representation and multimodal fusion with deep learning for AD\/MCI diagnosis. NeuroImage 101, 569\u2013582 (2014)","journal-title":"NeuroImage"},{"key":"55_CR16","unstructured":"Neelakantan, A., Vilnis, L., Le, Q.V., et al.: Adding gradient noise improves learning for very deep networks. arXiv preprint arXiv:1511.06807 (2015)"},{"key":"55_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/978-3-319-46723-8_14","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"T Xu","year":"2016","unstructured":"Xu, T., Zhang, H., Huang, X., Zhang, S., Metaxas, D.N.: Multimodal deep learning for cervical dysplasia diagnosis. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 115\u2013123. Springer, Cham (2016). doi:10.1007\/978-3-319-46723-8_14"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2212 MICCAI 2017"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-66179-7_55","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,5]],"date-time":"2022-09-05T00:13:44Z","timestamp":1662336824000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-66179-7_55"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319661780","9783319661797"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-66179-7_55","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2017]]},"assertion":[{"value":"4 September 2017","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":"Quebec City, QC","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":"2017","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2017","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2017","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2017","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.miccai2017.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}