{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T09:42:25Z","timestamp":1759570945716,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032054609","type":"print"},{"value":"9783032054616","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-032-05461-6_36","type":"book-chapter","created":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T09:08:04Z","timestamp":1759568884000},"page":"555-570","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel AI Approach for\u00a0the\u00a0Diagnosis of\u00a0Alzheimer\u2019s Disease from\u00a0Multi-modal Incomplete Data"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9910-8538","authenticated-orcid":false,"given":"Veronica","family":"Buttaro","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5061-2010","authenticated-orcid":false,"given":"Giuseppe","family":"Lamanna","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donato","family":"Massaro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4435-6602","authenticated-orcid":false,"given":"Claudio B.","family":"Caporusso","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2520-3616","authenticated-orcid":false,"given":"Gianvito","family":"Pio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6690-7583","authenticated-orcid":false,"given":"Michelangelo","family":"Ceci","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,5]]},"reference":[{"key":"36_CR1","doi-asserted-by":"crossref","unstructured":"Abed, M.T., Fatema, U., Nabil, S.A., Alam, M.A., Reza, M.T.: Alzheimer\u2019s disease prediction using convolutional neural network models leveraging pre-existing architecture and transfer learning. In: ICIEV & icIVPR, pp.\u00a01\u20136 (2020)","DOI":"10.1109\/ICIEVicIVPR48672.2020.9306649"},{"issue":"1","key":"36_CR2","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1137\/S0097539701398375","volume":"32","author":"P Auer","year":"2002","unstructured":"Auer, P., Cesa-Bianchi, N., Freund, Y., Schapire, R.E.: The nonstochastic multiarmed bandit problem. SIAM J. Comput. 32(1), 48\u201377 (2002)","journal-title":"SIAM J. Comput."},{"issue":"2","key":"36_CR3","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","volume":"41","author":"T Baltru\u0161aitis","year":"2018","unstructured":"Baltru\u0161aitis, T., Ahuja, C., Morency, L.P.: Multimodal machine learning: a survey and taxonomy. IEEE Trans. Pattern Anal. Mach. Intell. 41(2), 423\u2013443 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"36_CR4","unstructured":"Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics), 1 edn. Springer, Heidelberg (2007)"},{"issue":"3","key":"36_CR5","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1148\/radiology.182.3.1535892","volume":"182","author":"M Brant-Zawadzki","year":"1992","unstructured":"Brant-Zawadzki, M., Gillan, G.D., Nitz, W.R.: Mp rage: a three-dimensional, t1-weighted, gradient-echo sequence-initial experience in the brain. Radiology 182(3), 769\u2013775 (1992)","journal-title":"Radiology"},{"key":"36_CR6","unstructured":"Cardoso, M.J., Li, W., Brown, R., et\u00a0al.: Monai: an open-source framework for deep learning in healthcare (2022). https:\/\/arxiv.org\/abs\/2211.02701"},{"key":"36_CR7","doi-asserted-by":"crossref","unstructured":"Ceci, M., Pio, G., Kuzmanovski, V., D\u017eeroski, S.: Semi-supervised multi-view learning for gene network reconstruction. PLOS ONE 10(12), 1\u201327 (2015)","DOI":"10.1371\/journal.pone.0144031"},{"key":"36_CR8","doi-asserted-by":"crossref","unstructured":"Davis, D.H., Creavin, S.T., Noel-Storr, A., et\u00a0al.: Neuropsychological tests for the diagnosis of alzheimer\u2019s disease dementia and other dementias: a generic protocol for cross-sectional and delayed-verification studies. Cochrane Datab. Syst. Rev. (3), CD010460 (2013)","DOI":"10.1002\/14651858.CD010460"},{"key":"36_CR9","doi-asserted-by":"crossref","unstructured":"Elfwing, S.: Neural information processing. Theory and algorithms, p.\u00a0215 (2010)","DOI":"10.1007\/978-3-642-17537-4_27"},{"issue":"2","key":"36_CR10","doi-asserted-by":"publisher","first-page":"204","DOI":"10.2967\/jnumed.115.163717","volume":"57","author":"T Grimmer","year":"2016","unstructured":"Grimmer, T., et al.: Visual versus fully automated analyses of 18f-fdg and amyloid pet for prediction of dementia due to alzheimer disease in mild cognitive impairment. J. Nucl. Med. 57(2), 204\u2013207 (2016)","journal-title":"J. Nucl. Med."},{"key":"36_CR11","doi-asserted-by":"crossref","unstructured":"Hara, K., Kataoka, H., Satoh, Y.: Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet? (2018). https:\/\/arxiv.org\/abs\/1711.09577","DOI":"10.1109\/CVPR.2018.00685"},{"key":"36_CR12","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.853294","volume":"10","author":"C Kavitha","year":"2022","unstructured":"Kavitha, C., Mani, V., Srividhya, S., Khalaf, O.I., Tavera Romero, C.A.: Early-stage alzheimer\u2019s disease prediction using machine learning models. Front. Public Health 10, 853294 (2022)","journal-title":"Front. Public Health"},{"key":"36_CR13","doi-asserted-by":"crossref","unstructured":"Lazli, L.: Machine learning classifiers based on dimensionality reduction techniques for the early diagnosis of alzheimer\u2019s disease using magnetic resonance imaging and positron emission tomography brain data. In: International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, pp. 117\u2013131 (2021)","DOI":"10.1007\/978-3-031-20837-9_10"},{"issue":"2","key":"36_CR14","first-page":"325","volume":"19","author":"Y Li","year":"2018","unstructured":"Li, Y., Wu, F.X., Ngom, A.: A review on machine learning principles for multi-view biological data integration. Brief. Bioinform. 19(2), 325\u2013340 (2018)","journal-title":"Brief. Bioinform."},{"issue":"1","key":"36_CR15","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1002\/jmri.22003","volume":"31","author":"JV Manj\u00f3n","year":"2010","unstructured":"Manj\u00f3n, J.V., Coup\u00e9, P., Mart\u00ed-Bonmat\u00ed, L., Collins, D.L., Robles, M.: Adaptive non-local means denoising of mr images with spatially varying noise levels. J. Magn. Reson. Imaging 31(1), 192\u2013203 (2010)","journal-title":"J. Magn. Reson. Imaging"},{"key":"36_CR16","unstructured":"Parcalabescu, L., Trost, N., Frank, A.: What is multimodality? arXiv preprint arXiv:2103.06304 (2021)"},{"key":"36_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"},{"key":"36_CR18","doi-asserted-by":"publisher","unstructured":"Rosa, D., Pellicani, A., Pio, G., D\u2019Elia, D., Ceci, M.: Exploiting microrna expression data for the diagnosis of disease conditions and the discovery of novel biomarkers. In: ISMIS 2024, pp. 77\u201386. Springer, Heidelberg (2024). https:\/\/doi.org\/10.1007\/978-3-031-62700-2_8","DOI":"10.1007\/978-3-031-62700-2_8"},{"issue":"1","key":"36_CR19","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1186\/s12859-024-05767-w","volume":"25","author":"A Simeon","year":"2024","unstructured":"Simeon, A., Radovanovi\u0107, M., Lon\u010dar-Turukalo, T., Ceci, M., Brdar, S., Pio, G.: Multi-class boosting for the analysis of multiple incomplete views on microbiome data. BMC Bioinf. 25(1), 188 (2024)","journal-title":"BMC Bioinf."},{"key":"36_CR20","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2021.637386","volume":"3","author":"J Song","year":"2021","unstructured":"Song, J., Zheng, J., Li, P., Lu, X., Zhu, G., Shen, P.: An effective multimodal image fusion method using mri and pet for alzheimer\u2019s disease diagnosis. Front. Digital Health 3, 637386 (2021)","journal-title":"Front. Digital Health"},{"key":"36_CR21","doi-asserted-by":"crossref","unstructured":"Stahlschmidt, S.R., Ulfenborg, B., Synnergren, J.: Multimodal deep learning for biomedical data fusion: a review. Brief. Bioinf. 23(2), bbab569 (2022)","DOI":"10.1093\/bib\/bbab569"},{"key":"36_CR22","doi-asserted-by":"crossref","unstructured":"Tang, X., Guo, Z., Chen, G., et\u00a0al.: A multimodal meta-analytical evidence of functional and structural brain abnormalities across alzheimer\u2019s disease spectrum. Ageing Res. Rev. 102240 (2024)","DOI":"10.1016\/j.arr.2024.102240"},{"issue":"7","key":"36_CR23","doi-asserted-by":"publisher","first-page":"832","DOI":"10.1016\/j.jalz.2015.04.004","volume":"11","author":"AW Toga","year":"2015","unstructured":"Toga, A.W., Crawford, K.L.: The alzheimer\u2019s disease neuroimaging initiative informatics core: a decade in review. Alzheimer\u2019s Dementia 11(7), 832\u2013839 (2015)","journal-title":"Alzheimer\u2019s Dementia"},{"key":"36_CR24","volume":"6","author":"PM Tuan","year":"2021","unstructured":"Tuan, P.M., Phan, T.L., Adel, M., Guedj, E., Trung, N.L.: Autoencoder-based feature ranking for alzheimer disease classification using pet image. Mach. Learn. Appl. 6, 100184 (2021)","journal-title":"Mach. Learn. Appl."},{"issue":"6","key":"36_CR25","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","volume":"29","author":"NJ Tustison","year":"2010","unstructured":"Tustison, N.J., et al.: N4itk: improved n3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310\u20131320 (2010)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"36_CR26","doi-asserted-by":"publisher","first-page":"9068","DOI":"10.1038\/s41598-021-87564-6","volume":"11","author":"NJ Tustison","year":"2021","unstructured":"Tustison, N.J., Cook, P.A., Holbrook, A.J., et al.: The antsx ecosystem for quantitative biological and medical imaging. Sci. Rep. 11(1), 9068 (2021)","journal-title":"Sci. Rep."},{"issue":"1","key":"36_CR27","doi-asserted-by":"publisher","first-page":"3254","DOI":"10.1038\/s41598-020-74399-w","volume":"11","author":"J Venugopalan","year":"2021","unstructured":"Venugopalan, J., Tong, L., Hassanzadeh, H.R., Wang, M.D.: Multimodal deep learning models for early detection of alzheimer\u2019s disease stage. Sci. Rep. 11(1), 3254 (2021)","journal-title":"Sci. Rep."},{"key":"36_CR28","unstructured":"Williams, J.A., Weakley, A., Cook, D.J., Schmitter-Edgecombe, M.: Machine learning techniques for diagnostic differentiation of mild cognitive impairment and dementia. In: Workshops at the 27th AAAI Conference on AI, vol.\u00a021 (2013)"},{"key":"36_CR29","doi-asserted-by":"crossref","unstructured":"Yagis, E., De\u00a0Herrera, A.G.S., Citi, L.: Convolutional autoencoder based deep learning approach for alzheimer\u2019s disease diagnosis using brain mri. In: 2021 IEEE CBMS, pp. 486\u2013491. IEEE (2021)","DOI":"10.1109\/CBMS52027.2021.00097"},{"issue":"6","key":"36_CR30","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1016\/S1874-1029(13)60052-X","volume":"39","author":"C Ying","year":"2013","unstructured":"Ying, C., Qi-Guang, M., Jia-Chen, L., Lin, G.: Advance and prospects of adaboost algorithm. Acta Automatica Sinica 39(6), 745\u2013758 (2013)","journal-title":"Acta Automatica Sinica"},{"issue":"6","key":"36_CR31","doi-asserted-by":"publisher","first-page":"3238","DOI":"10.3390\/ijerph19063238","volume":"19","author":"H Zhang","year":"2022","unstructured":"Zhang, H., Zhou, W., Zhang, D.: Direct medical costs of parkinson\u2019s disease in southern china: a cross-sectional study based on health insurance claims data in guangzhou city. Int. J. Environ. Res. Public Health 19(6), 3238 (2022)","journal-title":"Int. J. Environ. Res. Public Health"}],"container-title":["Lecture Notes in Computer Science","Discovery Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05461-6_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T09:08:12Z","timestamp":1759568892000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05461-6_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783032054609","9783032054616"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05461-6_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"5 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Discovery Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ljubljana","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovenia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dis2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ds2025.ijs.si\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}