{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:09:09Z","timestamp":1767319749720,"version":"3.48.0"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032095688","type":"print"},{"value":"9783032095695","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-09569-5_36","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:04:13Z","timestamp":1767319453000},"page":"360-369","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Tabular Data-Enhanced Multi-modal Alignment and\u00a0Synthesis for\u00a0Alzheimer\u2019s Disease Diagnosis"],"prefix":"10.1007","author":[{"given":"Weilin","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxiao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanwang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaicong","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shilun","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanbo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"issue":"3","key":"36_CR1","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.jalz.2011.03.008","volume":"7","author":"S Marilyn","year":"2011","unstructured":"Marilyn, S., et al.: The diagnosis of mild cognitive impairment due to Alzheimer\u2019s disease: recommendations from the national institute on aging-Alzheimer\u2019s association workgroups on diagnostic guidelines for Alzheimer\u2019s disease. Alzheimer\u2019s Dementia 7(3), 270\u2013279 (2011)","journal-title":"Alzheimer\u2019s Dementia"},{"issue":"5","key":"36_CR2","doi-asserted-by":"publisher","first-page":"a006148","DOI":"10.1101\/cshperspect.a006148","volume":"2","author":"R Tarawneh","year":"2012","unstructured":"Tarawneh, R., Holtzman, D.M.: The clinical problem of symptomatic Alzheimer disease and mild cognitive impairment. Cold Spring Harbor Perspect. Med. 2(5), a006148 (2012)","journal-title":"Cold Spring Harbor Perspect. Med."},{"issue":"3","key":"36_CR3","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/j.cpet.2017.02.005","volume":"12","author":"C Xia","year":"2017","unstructured":"Xia, C., Dickerson, B.C.: Multimodal PET imaging of amyloid and Tau pathology in Alzheimer disease and non-Alzheimer disease dementias. PET Clin. 12(3), 351\u2013359 (2017)","journal-title":"PET Clin."},{"key":"36_CR4","doi-asserted-by":"crossref","unstructured":"Taleb, A., Kirchler, M., Monti, R., Lippert, C.: Contig: self-supervised multimodal contrastive learning for medical imaging with genetics. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20 908\u201320 921 (2022)","DOI":"10.1109\/CVPR52688.2022.02024"},{"key":"36_CR5","doi-asserted-by":"crossref","unstructured":"Mellor, D., et al.: Determining appropriate screening tools and cut-points for cognitive impairment in an elderly Chinese sample. Psychol. Assess. 28(11) (2016)","DOI":"10.1037\/pas0000271"},{"key":"36_CR6","doi-asserted-by":"crossref","unstructured":"Polsterl, S., Sarasua, I., Gutierrez-Becker, B., Wachinger, C.: A wide and deep neural network for survival analysis from anatomical shape and tabular clinical data. In: Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD, pp. 453\u2013464 (2019)","DOI":"10.1007\/978-3-030-43823-4_37"},{"key":"36_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1007\/978-3-030-87196-3_6","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"B Dufumier","year":"2021","unstructured":"Dufumier, B., et al.: Contrastive learning with continuous proxy meta-data for 3D MRI classification. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 58\u201368. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87196-3_6"},{"key":"36_CR8","doi-asserted-by":"crossref","unstructured":"Guan, H., Wang, C., Tao, D.: MRI-based Alzheimer\u2019s disease prediction via distilling the knowledge in multi-modal data. NeuroImage 244 (2021)","DOI":"10.1016\/j.neuroimage.2021.118586"},{"key":"36_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1007\/978-3-030-87240-3_66","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"S P\u00f6lsterl","year":"2021","unstructured":"P\u00f6lsterl, S., Wolf, T.N., Wachinger, C.: Combining 3D image and tabular data via the dynamic affine feature map transform. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 688\u2013698. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87240-3_66"},{"key":"36_CR10","doi-asserted-by":"crossref","unstructured":"Perez, E., Strub, F., de Vries, H., Dumoulin, V., Courville, A.: FiLM: visual reasoning with a general conditioning layer. In: AAAI, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11671"},{"key":"36_CR11","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"He, K., et al.: A survey of large language models for healthcare: from data, technology, and applications to accountability and ethics. Inf. Fusion 118 (2025)","DOI":"10.1016\/j.inffus.2025.102963"},{"key":"36_CR13","doi-asserted-by":"publisher","first-page":"101871","DOI":"10.1016\/j.media.2020.101871","volume":"68","author":"H Peng","year":"2021","unstructured":"Peng, H., Gong, W., Beckmann, C.F., Vedaldi, A., Smith, S.M.: Accurate brain age prediction with lightweight deep neural networks. Med. Image Anal. 68, 101871 (2021)","journal-title":"Med. Image Anal."},{"issue":"1","key":"36_CR14","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1109\/JBHI.2024.3472011","volume":"29","author":"Y Zhang","year":"2025","unstructured":"Zhang, Y., Sun, K., Liu, Y., Xie, F., Guo, Q., Shen, D.: A modality-flexible framework for Alzheimer\u2019s disease diagnosis following clinical routine. IEEE J. Biomed. Health Inform. 29(1), 535\u2013546 (2025)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"36_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1007\/978-3-031-16449-1_29","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022","author":"M Mallya","year":"2022","unstructured":"Mallya, M., Hamarneh, M.: Deep multimodal guidance for medical image classification. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13437, pp. 298\u2013308. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16449-1_29"},{"key":"36_CR16","doi-asserted-by":"crossref","unstructured":"Yu, Q., et al.: A transformer-based unified multimodal framework for Alzheimer\u2019s disease assessment. Comput. Biol. Med. 180 (2024)","DOI":"10.1016\/j.compbiomed.2024.108979"},{"key":"36_CR17","doi-asserted-by":"crossref","unstructured":"Liu, L., et al.: Cascaded multi-modal mixing transformers for Alzheimer\u2019s disease classification with incomplete data. NeuroImage 120267 (2023)","DOI":"10.1016\/j.neuroimage.2023.120267"},{"issue":"10","key":"36_CR18","doi-asserted-by":"publisher","first-page":"6839","DOI":"10.1109\/TPAMI.2021.3091214","volume":"44","author":"Y Pan","year":"2021","unstructured":"Pan, Y., Liu, M., Xia, Y., Shen, D.: Disease-image-specific learning for diagnosisoriented neuroimage synthesis with incomplete multi-modality data. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6839\u20136853 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"36_CR19","doi-asserted-by":"crossref","unstructured":"Zhou, R., Zhou, H., Shen, L., Chen, B.Y., Zhang, Y., He, L.: Integrating multimodal contrastive learning and cross-modal attention for Alzheimer\u2019s disease prediction in brain imaging genetics. In: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Istanbul, Turkiye, pp. 1806\u20131811 (2023)","DOI":"10.1109\/BIBM58861.2023.10385864"},{"key":"36_CR20","doi-asserted-by":"crossref","unstructured":"Zhao, L., Zhang, J., Xu, B., Yang, Y., Zhang, Y., Ma, R.: Multimodal contrastive learning with neuroimaging and cognitive tests for Alzheimer\u2019s disease diagnosis. In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Lisbon, Portugal (2024)","DOI":"10.1109\/BIBM62325.2024.10822064"},{"key":"36_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/978-3-031-72104-5_5","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024","author":"Z Ou","year":"2024","unstructured":"Ou, Z., Jiang, C., Liu, Y., Zhang, Y., Cui, Z., Shen, D.: A graph-embedded latent space learning and clustering framework for incomplete multimodal multiclass Alzheimer\u2019s disease diagnosis. In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15007, pp. 45\u201355. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72104-5_5"},{"key":"36_CR22","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1038\/s44172-024-00245-w","volume":"3","author":"K Sun","year":"2024","unstructured":"Sun, K., Zhang, Y., Liu, J., et al.: Achieving multi-modal brain disease diagnosis performance using only single-modal images through generative AI. Commun. Eng. 3, 96 (2024)","journal-title":"Commun. Eng."}],"container-title":["Lecture Notes in Computer Science","Applications of Medical Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-09569-5_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:04:15Z","timestamp":1767319455000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-09569-5_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032095688","9783032095695"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-09569-5_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AMAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Applications of Medical AI","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","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":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"amai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/amai2025\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}