{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:22:56Z","timestamp":1792783376946,"version":"4.5.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"3-4","license":[{"start":{"date-parts":[[2018,3,23]],"date-time":"2018-03-23T00:00:00Z","timestamp":1521763200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61375112, 61773263, U1504606"],"award-info":[{"award-number":["61375112, 61773263, U1504606"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the National Key Research and Development Program of China","award":["2016YFC0100903"],"award-info":[{"award-number":["2016YFC0100903"]}]},{"name":"SMC Excellent Young Faculty program of SJTU"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neuroinform"],"published-print":{"date-parts":[[2018,10]]},"DOI":"10.1007\/s12021-018-9370-4","type":"journal-article","created":{"date-parts":[[2018,3,23]],"date-time":"2018-03-23T12:29:08Z","timestamp":1521808148000},"page":"295-308","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":361,"title":["Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer\u2019s Disease Diagnosis"],"prefix":"10.1007","volume":"16","author":[{"name":"the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6496-670X","authenticated-orcid":false,"given":"Manhua","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danni","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kundong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaping","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,3,23]]},"reference":[{"key":"9370_CR1","unstructured":"Adrien, P.A.G.M. (2015). Predicting Alzheimer\u2019s disease: a neuroimaging study with 3D convolutional neural networks. arXiv:1502.02506 [cs.CV]."},{"key":"9370_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artmed.2016.06.003","volume":"71","author":"A Alberdi","year":"2016","unstructured":"Alberdi, A., Aztiria, A., & Basarab, A. (2016). On the early diagnosis of Alzheimer's disease from multimodal signals: A survey. Artificial Intelligence in Medicine, 71, 1\u201329.","journal-title":"Artificial Intelligence in Medicine"},{"key":"9370_CR3","doi-asserted-by":"crossref","unstructured":"Cabral, C., Silveira, M., Neuroimaging, A.S.D. (2013). Classification of Alzheimer\u2019s disease from FDG-PET images using favourite class ensembles. 2013 35th Annual International Conference of the Ieee Engineering in Medicine and Biology Society (Embc), pp. 2477\u20132480.","DOI":"10.1109\/EMBC.2013.6610042"},{"key":"9370_CR4","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1007\/s11682-015-9356-x","volume":"9","author":"B Cheng","year":"2015","unstructured":"Cheng, B., Liu, M., Suk, H. I., Shen, D., & Zhang, D. (2015). Multimodal manifold-regularized transfer learning for MCI conversion prediction. Brain Imaging and Behavior, 9, 913\u2013926.","journal-title":"Brain Imaging and Behavior"},{"key":"9370_CR5","doi-asserted-by":"crossref","unstructured":"Gerardin, E., Chetelat, G., Chupin, M., Cuingnet, R., Desgranges, B., Kim, H. S., Niethammer, M., Dubois, B., Lehericy, S., Garnero, L., Eustache, F., Colliot, O., & Initi, A.s. D. N. (2009). Multidimensional classification of hippocampal shape features discriminates Alzheimer\u2019s disease and mild cognitive impairment from normal aging. NeuroImage, 47, 1476\u20131486.","DOI":"10.1016\/j.neuroimage.2009.05.036"},{"key":"9370_CR6","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep residual learning for image recognition. pp. 770\u2013778."},{"key":"9370_CR7","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.neuroimage.2009.05.056","volume":"48","author":"C Hinrichs","year":"2009","unstructured":"Hinrichs, C., Singh, V., Mukherjee, L., Xu, G., Chung, M. K., & Johnson, S. C. (2009). Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset. NeuroImage, 48, 138\u2013149.","journal-title":"NeuroImage"},{"key":"9370_CR8","doi-asserted-by":"crossref","unstructured":"Hosseini-Asl, E., Keynton, R., & El-Baz, A. (2016). Alzheimer\u2019s disease diagnostics by adaptation of 3D convolutional network. 2016 I.E. International Conference on Image Processing (ICIP), pp 126\u2013130.","DOI":"10.1109\/ICIP.2016.7532332"},{"issue":"2","key":"9370_CR9","first-page":"333","volume":"26","author":"K Ishii","year":"2005","unstructured":"Ishii, K., Kawachi, T., Sasaki, H., Kono, A. K., Fukuda, T., Kojima, Y., & Mori, E. (2005). Voxel-based morphometric comparison between early- and late-onset mild Alzheimer\u2019s disease and assessment of diagnostic performance of z score images. AJNR American Journal of Neuroradiology, 26(2), 333\u2013340.","journal-title":"AJNR American Journal of Neuroradiology"},{"key":"9370_CR10","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1002\/jmri.21049","volume":"27","author":"CR Jack Jr","year":"2008","unstructured":"Jack Jr., C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L Whitwell, J., Ward, C., Dale, A. M., Felmlee, J. P., Gunter, J. L., Hill, D. L., Killiany, R., Schuff, N., Fox-Bosetti, S., Lin, C., Studholme, C., DeCarli, C. S., Krueger, G., Ward, H. A., Metzger, G. J., Scott, K. T., Mallozzi, R., Blezek, D., Levy, J., Debbins, J. P., Fleisher, A. S., Albert, M., Green, R., Bartzokis, G., Glover, G., Mugler, J., & Weiner, M. W. (2008). The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging: JMRI, 27, 685\u2013691.","journal-title":"Journal of Magnetic Resonance Imaging: JMRI"},{"key":"9370_CR11","doi-asserted-by":"publisher","first-page":"S717","DOI":"10.1016\/S1053-8119(18)31550-7","volume":"7","author":"N Kabani","year":"1998","unstructured":"Kabani, N., MacDonald, D., Holmes, C. J., & Evans, A. (1998). A 3D atlas of the human brain. NeuroImage, 7, S717.","journal-title":"NeuroImage"},{"key":"9370_CR12","doi-asserted-by":"crossref","unstructured":"Kloppel, S., Stonnington, C.M., Chu, C., Draganski, B., Scahill, R.I., Rohrer, J.D., Fox, N.C., Jack Jr, C.R., Ashburner, J., & Frackowiak, R.S.J. (2008). Automatic classification of MR scans in Alzheimer\u2019s disease Brain 131(Pt 3):681\u2013689.","DOI":"10.1093\/brain\/awm319"},{"key":"9370_CR13","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E. (2012). ImageNet classification with deep convolutional neural networks. International Conference on Neural Information Processing Systems, pp. 1097\u20131105."},{"key":"9370_CR14","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y L\u00e9cun","year":"1998","unstructured":"L\u00e9cun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proc IEEE, 86, 2278\u20132324.","journal-title":"Proc IEEE"},{"key":"9370_CR15","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.neurobiolaging.2006.09.013","volume":"29","author":"JP Lerch","year":"2008","unstructured":"Lerch, J. P., Pruessner, J., Zijdenbos, A. P., Collins, D. L., Teipel, S. J., Hampel, H., & Evans, A. C. (2008). Automated cortical thickness measurements from MRI can accurately separate Alzheimer's patients from normal elderly controls. Neurobiology of Aging, 29, 23\u201330.","journal-title":"Neurobiology of Aging"},{"key":"9370_CR16","doi-asserted-by":"crossref","unstructured":"Li, R., Zhang, W., Suk, H.I., Wang, L., Li, J., Shen, D., Ji, S., (2014). Deep learning based imaging data completion for improved brain disease diagnosis. Medical image computing and computer-assisted intervention: MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 17, 305\u2013312.","DOI":"10.1007\/978-3-319-10443-0_39"},{"key":"9370_CR17","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Roychowdhury, A., & Maji, S. (2015). Bilinear CNN models for fine-grained visual recognition. IEEE International Conference on Computer Vision, Santiago, Chile, pp 1449\u20131457.","DOI":"10.1109\/ICCV.2015.170"},{"key":"9370_CR18","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.1109\/TBME.2014.2372011","volume":"62","author":"S Liu","year":"2015","unstructured":"Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., Feng, D., & Fulham, M. J. (2015). Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease. IEEE Transactions on Biomedical Engineering, 62, 1132\u20131140.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"9370_CR19","unstructured":"Lu, S., Xia, Y., Cai, T.W., & Feng, D.D. (2015). Semi-supervised manifold learning with affinity regularization for Alzheimer's disease identification using positron emission tomography imaging. 2015 37th Annual International Conference of the Ieee Engineering in Medicine and Biology Society (Embc), pp. 2251\u20132254."},{"key":"9370_CR20","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1177\/1533317508328602","volume":"24","author":"L Minati","year":"2009","unstructured":"Minati, L., Edginton, T., Bruzzone, M. G., & Giaccone, G. (2009). Reviews: Current concepts in Alzheimer's disease: A multidisciplinary review. American Journal of Alzheimers Disease & Other Dementias, 24, 95\u2013121.","journal-title":"American Journal of Alzheimers Disease & Other Dementias"},{"key":"9370_CR21","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","volume":"19","author":"D Shen","year":"2017","unstructured":"Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19, 221.","journal-title":"Annual Review of Biomedical Engineering"},{"key":"9370_CR22","doi-asserted-by":"crossref","unstructured":"Silveira M, Marques, J. (2010). Boosting Alzheimer disease diagnosis using PET images. 20th IEEE international conference on pattern recognition (ICPR), pp. 2556\u20132559.","DOI":"10.1109\/ICPR.2010.626"},{"key":"9370_CR23","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/42.668698","volume":"17","author":"JG Sled","year":"1998","unstructured":"Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998). A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging, 17, 87\u201397.","journal-title":"IEEE Trans Med Imaging"},{"key":"9370_CR24","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929\u20131958.","journal-title":"Journal of Machine Learning Research"},{"key":"9370_CR25","unstructured":"Suk, H.I., Shen, D., 2013. Deep learning-based feature representation for AD\/MCI classification. Medical image computing and computer-assisted intervention: MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 16, 583\u2013590."},{"key":"9370_CR26","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., & Shen, D. (2014). Hierarchical feature representation and multimodal fusion with deep learning for AD\/MCI diagnosis. NeuroImage, 101, 569\u2013582.","journal-title":"NeuroImage"},{"key":"9370_CR27","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1007\/s00429-013-0687-3","volume":"220","author":"HI Suk","year":"2015","unstructured":"Suk, H. I., Lee, S. W., & Shen, D. (2015). Latent feature representation with stacked auto-encoder for AD\/MCI diagnosis. Brain Structure and Function, 220, 841\u2013859.","journal-title":"Brain Structure and Function"},{"issue":"3","key":"9370_CR28","first-page":"635","volume":"14","author":"Y Wang","year":"2011","unstructured":"Wang, Y., Nie, J., Yap, P. T., Shi, F., Guo, L., & Shen, D. (2011). Robust deformable-surface-based skull-stripping for large-scale studies. Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI, 14(3), 635\u2013642.","journal-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI"},{"issue":"2","key":"9370_CR29","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1088\/0031-9155\/61\/2\/791","volume":"61","author":"Y Wang","year":"2016","unstructured":"Wang, Y., Zhang, P., An, L., Ma, G., Kang, J., Shi, F., Wu, X., Zhou, J., Lalush, D. S., & Lin, W. (2016). Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation. Physics in Medicine and Biology, 61(2), 791\u2013812.","journal-title":"Physics in Medicine and Biology"},{"key":"9370_CR30","doi-asserted-by":"crossref","unstructured":"Weinzaepfel, P., Harchaoui, Z., & Schmid, C. (2015). Learning to track for spatio-temporal action localization. pp. 3164\u20133172.","DOI":"10.1109\/ICCV.2015.362"},{"key":"9370_CR31","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1109\/TBME.2016.2564440","volume":"64","author":"W Yan","year":"2017","unstructured":"Yan, W., Ma, G., Le, A., Feng, S., Pei, Z., Xi, W., Zhou, J., & Shen, D. (2017). Semi-supervised tripled dictionary learning for standard-dose PET image prediction using low-dose PET and multimodal MRI. IEEE Transactions on Biomedical Engineering, 64, 569\u2013579.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"9370_CR32","unstructured":"Zeiler, M.D. (2012). ADADELTA: An adaptive learning rate method. Computer Science."},{"key":"9370_CR33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10590-1_53","volume-title":"Visualizing and understanding convolutional networks","author":"MD Zeiler","year":"2014","unstructured":"Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. Basel: Springer International Publishing."},{"key":"9370_CR34","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1016\/j.neuroimage.2011.01.008","volume":"55","author":"D Zhang","year":"2011","unstructured":"Zhang, D., Wang, Y., Zhou, L., Yuan, H., & Shen, D. (2011). Multimodal classification of Alzheimer's disease and mild cognitive impairment. NeuroImage, 55, 856\u2013867.","journal-title":"NeuroImage"}],"container-title":["Neuroinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12021-018-9370-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-018-9370-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-018-9370-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,3,22]],"date-time":"2019-03-22T20:45:55Z","timestamp":1553287555000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s12021-018-9370-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,3,23]]},"references-count":34,"journal-issue":{"issue":"3-4","published-print":{"date-parts":[[2018,10]]}},"alternative-id":["9370"],"URL":"https:\/\/doi.org\/10.1007\/s12021-018-9370-4","relation":{},"ISSN":["1539-2791","1559-0089"],"issn-type":[{"value":"1539-2791","type":"print"},{"value":"1559-0089","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,3,23]]},"assertion":[{"value":"23 March 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}