{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T17:13:50Z","timestamp":1761844430874},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319105802"},{"type":"electronic","value":"9783319105819"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014]]},"DOI":"10.1007\/978-3-319-10581-9_15","type":"book-chapter","created":{"date-parts":[[2014,9,5]],"date-time":"2014-09-05T14:05:03Z","timestamp":1409925903000},"page":"117-124","source":"Crossref","is-referenced-by-count":35,"title":["Deep Learning of Image Features from Unlabeled Data for Multiple Sclerosis Lesion Segmentation"],"prefix":"10.1007","author":[{"given":"Youngjin","family":"Yoo","sequence":"first","affiliation":[]},{"given":"Tom","family":"Brosch","sequence":"additional","affiliation":[]},{"given":"Anthony","family":"Traboulsee","sequence":"additional","affiliation":[]},{"given":"David K. B.","family":"Li","sequence":"additional","affiliation":[]},{"given":"Roger","family":"Tam","sequence":"additional","affiliation":[]}],"member":"297","reference":[{"issue":"1","key":"15_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2012.09.004","volume":"17","author":"Garc\u00eda-Lorenzo","year":"2013","unstructured":"Garc\u00eda-Lorenzo, et al.: Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Med. Image Anal.\u00a017(1), 1\u201318 (2013)","journal-title":"Med. Image Anal."},{"issue":"2","key":"15_CR2","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1016\/j.neuroimage.2011.03.080","volume":"57","author":"E. Geremia","year":"2011","unstructured":"Geremia, E., et al.: Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images. NeuroImage\u00a057(2), 378\u2013390 (2011)","journal-title":"NeuroImage"},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Morra, J., et al.: Automatic segmentation of MS lesions using a contextual model for the MICCAI grand challenge. In: MS Lesion Segmentation Challenge (MICCAI Workshop), pp. 1\u20137 (2008)","DOI":"10.54294\/sljnc2"},{"issue":"2","key":"15_CR4","doi-asserted-by":"publisher","first-page":"1524","DOI":"10.1016\/j.neuroimage.2009.09.005","volume":"49","author":"N. Shiee","year":"2010","unstructured":"Shiee, N., et al.: A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions. NeuroImage\u00a049(2), 1524\u20131535 (2010)","journal-title":"NeuroImage"},{"key":"15_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/978-3-642-22092-0_16","volume-title":"Information Processing in Medical Imaging","author":"A. Montillo","year":"2011","unstructured":"Montillo, A., Shotton, J., Winn, J., Iglesias, J.E., Metaxas, D., Criminisi, A.: Entangled decision forests and their application for semantic segmentation of CT images. In: Sz\u00e9kely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol.\u00a06801, pp. 184\u2013196. Springer, Heidelberg (2011)"},{"key":"15_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/978-3-642-24319-6_23","volume-title":"Machine Learning in Medical Imaging","author":"M. Yaqub","year":"2011","unstructured":"Yaqub, M., Javaid, M.K., Cooper, C., Noble, J.A.: Improving the classification accuracy of the classic RF method by intelligent feature selection and weighted voting of trees with application to medical image segmentation. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds.) MLMI 2011. LNCS, vol.\u00a07009, pp. 184\u2013192. Springer, Heidelberg (2011)"},{"issue":"1","key":"15_CR7","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/42.668698","volume":"17","author":"J.G. Sled","year":"1998","unstructured":"Sled, J.G., et al.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE T. Med. Imaging\u00a017(1), 87\u201397 (1998)","journal-title":"IEEE T. Med. Imaging"},{"issue":"3","key":"15_CR8","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1002\/hbm.10062","volume":"17","author":"S.M. Smith","year":"2002","unstructured":"Smith, S.M.: Fast robust automated brain extraction. Human Brain Mapping\u00a017(3), 143\u2013155 (2002)","journal-title":"Human Brain Mapping"},{"key":"15_CR9","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. University of Toronto, Tech. Rep. (2009)"},{"key":"15_CR10","unstructured":"Hinton, G.: A practical guide to training restricted Boltzmann machines. University of Toronto, Tech. Rep. (2010)"},{"issue":"7","key":"15_CR11","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"G. Hinton","year":"2006","unstructured":"Hinton, G., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Computation\u00a018(7), 1527\u20131554 (2006)","journal-title":"Neural Computation"},{"issue":"1","key":"15_CR12","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L. Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Machine Learning\u00a045(1), 5\u201332 (2001)","journal-title":"Machine Learning"},{"key":"15_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1007\/978-3-642-40811-3_92","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2013","author":"N. Weiss","year":"2013","unstructured":"Weiss, N., Rueckert, D., Rao, A.: Multiple sclerosis lesion segmentation using dictionary learning and sparse coding. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part I. LNCS, vol.\u00a08149, pp. 735\u2013742. Springer, Heidelberg (2013)"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Souplet, J.C., et al.: An automatic segmentation of T2-FLAIR multiple sclerosis lesions. In: MS Lesion Segmentation Challenge, MICCAI Workshop (2008)","DOI":"10.54294\/6eyg0w"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-10581-9_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,16]],"date-time":"2022-04-16T11:16:18Z","timestamp":1650107778000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-10581-9_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014]]},"ISBN":["9783319105802","9783319105819"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-10581-9_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2014]]}}}