{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:46:14Z","timestamp":1776887174491,"version":"3.51.2"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164309","type":"print"},{"value":"9783031164316","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-16431-6_4","type":"book-chapter","created":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T21:02:58Z","timestamp":1663189378000},"page":"34-43","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["3D Global Fourier Network for Alzheimer\u2019s Disease Diagnosis Using Structural MRI"],"prefix":"10.1007","author":[{"given":"Shengjie","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bohan","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haibo","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziqi","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao-Yong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"key":"4_CR1","doi-asserted-by":"crossref","unstructured":"Knopman, D.S., et al.: Alzheimer disease. In: Nature reviews Disease Primers, vol. 7.1, pp. 1\u201321 (2021)","DOI":"10.1038\/s41572-021-00269-y"},{"issue":"3","key":"4_CR2","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1148\/radiol.2020192541","volume":"296","author":"A Damulina","year":"2020","unstructured":"Damulina, A., et al.: Cross-sectional and longitudinal assessment of brain iron level in Alzheimer disease using 3-T MRI. Radiology 296(3), 619\u2013626 (2020)","journal-title":"Radiology"},{"key":"4_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101694","volume":"63","author":"J Wen","year":"2020","unstructured":"Wen, J., et al.: Convolutional neural networks for classification of Alzheimer\u2019s disease: overview and reproducible evaluation. Med. Image Anal. 63, 101694 (2020)","journal-title":"Med. Image Anal."},{"key":"4_CR4","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.neucom.2018.09.001","volume":"320","author":"N Zeng","year":"2018","unstructured":"Zeng, N., et al.: A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer\u2019s disease. Neurocomputing 320, 195\u2013202 (2018)","journal-title":"Neurocomputing"},{"key":"4_CR5","doi-asserted-by":"publisher","first-page":"307","DOI":"10.3389\/fnins.2015.00307","volume":"9","author":"C Salvatore","year":"2015","unstructured":"Salvatore, C., et al.: Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer\u2019s disease: a machine learning approach. Front. Neurosci. 9, 307 (2015)","journal-title":"Front. Neurosci."},{"issue":"2","key":"4_CR6","doi-asserted-by":"publisher","first-page":"766","DOI":"10.1016\/j.neuroimage.2010.06.013","volume":"56","author":"R Cuingnet","year":"2011","unstructured":"Cuingnet, R., et al.: Automatic classification of patients with Alzheimer\u2019s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 56(2), 766\u2013781 (2011)","journal-title":"Neuroimage"},{"key":"4_CR7","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1016\/j.neuroimage.2012.09.058","volume":"65","author":"SF Eskildsen","year":"2013","unstructured":"Eskildsen, S.F., et al.: Prediction of Alzheimer\u2019s disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning. Neuroimage 65, 511\u2013521 (2013)","journal-title":"Neuroimage"},{"key":"4_CR8","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.compbiomed.2017.10.002","volume":"91","author":"P Cao","year":"2017","unstructured":"Cao, P., et al.: Nonlinearity-aware based dimensionality reduction and over-sampling for AD\/MCI classification from MRI measures. Comput. Biol. Med. 91, 21\u201337 (2017)","journal-title":"Comput. Biol. Med."},{"key":"4_CR9","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.jneumeth.2018.01.003","volume":"302","author":"L S\u00f8rensen","year":"2018","unstructured":"S\u00f8rensen, L., Nielsen, M., Initiative, A.D.N., et al.: Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination. J. Neurosci. Methods 302, 66\u201374 (2018)","journal-title":"J. Neurosci. Methods"},{"issue":"5","key":"4_CR10","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1016\/j.media.2014.04.006","volume":"18","author":"T Tong","year":"2014","unstructured":"Tong, T., et al.: Multiple instance learning for classification of dementia in brain MRI. Med. Image Anal. 18(5), 808\u2013818 (2014)","journal-title":"Med. Image Anal."},{"key":"4_CR11","unstructured":"Khvostikov, A., et al.: 3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies. arXiv preprint arXiv:1801.05968 (2018)"},{"key":"4_CR12","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.media.2017.10.005","volume":"43","author":"M Liu","year":"2018","unstructured":"Liu, M., et al.: Landmark-based deep multi-instance learning for brain disease diagnosis. Med. Image Anal. 43, 157\u2013168 (2018)","journal-title":"Med. Image Anal."},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Zhou, B., et al.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"},{"issue":"4","key":"4_CR14","doi-asserted-by":"publisher","first-page":"880","DOI":"10.1109\/TPAMI.2018.2889096","volume":"42","author":"C Lian","year":"2018","unstructured":"Lian, C., et al.: Hierarchical fully convolutional network for joint atrophy localization and Alzheimer\u2019s disease diagnosis using structural MRI. IEEE Trans. Pattern Anal. Mach. Intell. 42(4), 880\u2013893 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"9","key":"4_CR15","doi-asserted-by":"publisher","first-page":"2354","DOI":"10.1109\/TMI.2021.3077079","volume":"40","author":"W Zhu","year":"2021","unstructured":"Zhu, W., et al.: Dual attention multi-instance deep learning for Alzheimer\u2019s disease diagnosis with structural MRI. IEEE Trans. Med. Imaging 40(9), 2354\u20132366 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"4_CR16","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.1093\/brain\/awaa137","volume":"143","author":"S Qiu","year":"2020","unstructured":"Qiu, S., et al.: Development and validation of an interpretable deep learning framework for Alzheimer\u2019s disease classification. Brain 143(6), 1920\u20131933 (2020)","journal-title":"Brain"},{"key":"4_CR17","unstructured":"Li, H., Habes, M., Fan, Y.: Deep ordinal ranking for multi-category diagnosis of Alzheimer\u2019s disease using hippocampal MRI data. arXiv preprint arXiv:1709.01599 (2017)"},{"key":"4_CR18","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.compmedimag.2018.09.009","volume":"70","author":"F Li","year":"2018","unstructured":"Li, F., Liu, M., Initiative, A.D.N., et al.: Alzheimer\u2019s disease diagnosis based on multiple cluster dense convolutional networks. Comput. Med. Imaging Graph. 70, 101\u2013110 (2018)","journal-title":"Comput. Med. Imaging Graph."},{"key":"4_CR19","doi-asserted-by":"publisher","first-page":"777","DOI":"10.3389\/fnins.2018.00777","volume":"12","author":"W Lin","year":"2018","unstructured":"Lin, W., et al.: Convolutional neural networks-based MRI image analysis for the Alzheimer\u2019s disease prediction from mild cognitive impairment. Front. Neurosci. 12, 777 (2018)","journal-title":"Front. Neurosci."},{"key":"4_CR20","first-page":"980","volume":"34","author":"Y Rao","year":"2021","unstructured":"Rao, Y., et al.: Global filter networks for image classification. Adv. Neural. Inf. Process. Syst. 34, 980\u2013993 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"4_CR21","doi-asserted-by":"crossref","unstructured":"He, K., et al.: Masked autoencoders are scalable vision learners (2021). arXiv: 2111.06377 [cs.CV]","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"4_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1007\/978-3-030-00934-2_29","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"HD Couture","year":"2018","unstructured":"Couture, H.D., Marron, J.S., Perou, C.M., Troester, M.A., Niethammer, M.: Multiple instance learning for\u00a0heterogeneous images: training a\u00a0CNN for histopathology. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 254\u2013262. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_29"},{"issue":"6","key":"4_CR23","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1006\/nimg.2000.0582","volume":"11","author":"J Ashburner","year":"2000","unstructured":"Ashburner, J., Friston, K.J.: Voxel-based morphometry-the methods. Neuroimage 11(6), 805\u2013821 (2000)","journal-title":"Neuroimage"},{"key":"4_CR24","doi-asserted-by":"crossref","unstructured":"Kruthika, K.R. HD Maheshappa, Alzheimer\u2019s disease neuroimaging initiative. CBIR System using Capsule Networks and 3D CNN for Alzheimer\u2019s disease diagnosis. Inf. Med. Unlocked 14, 59\u201368 (2019)","DOI":"10.1016\/j.imu.2018.12.001"},{"key":"4_CR25","doi-asserted-by":"crossref","unstructured":"He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"4_CR26","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"4_CR27","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1016\/j.nicl.2017.12.023","volume":"18","author":"LM Gerischer","year":"2018","unstructured":"Gerischer, L.M., et al.: Combining viscoelasticity, diffusivity and volume of the hippocampus for the diagnosis of Alzheimer\u2019s disease based on magnetic resonance imaging. NeuroImage Clin. 18, 485\u2013493 (2018)","journal-title":"NeuroImage Clin."},{"key":"4_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2019.101663","volume":"80","author":"W Shao","year":"2020","unstructured":"Shao, W., et al.: Hypergraph based multi-task feature selection for multimodal classification of Alzheimer\u2019s disease. Comput. Med. Imaging Graph. 80, 101663 (2020)","journal-title":"Comput. Med. Imaging Graph."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16431-6_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T20:12:54Z","timestamp":1710360774000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16431-6_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164309","9783031164316"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16431-6_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 September 2022","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":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","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":"Microsoft Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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","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":"5","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)"}}]}}