{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T17:54:21Z","timestamp":1785779661488,"version":"3.56.0"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000774","name":"The University of Newcastle","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000774","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Alzheimer\u2019s disease (AD) classification from structural magnetic resonance imaging (MRI) remains challenging, particularly when distinguishing mild cognitive impairment (MCI) from both cognitively normal (CN) ageing and established AD. Multimodal approaches that combine imaging with clinical information are promising, but most confine the influence of clinical variables to the classifier head, so biomarkers cannot shape spatial feature extraction inside the imaging encoder. We propose a bimodal deep learning framework operating on two input sources: 3D T1-weighted structural MRI and tabular clinical assessment scores. A biomarker encoder maps the clinical scores into a conditioning representation that modulates vision transformer patch tokens through spatial rescaling and cross-attention throughout feature extraction. Within selected transformer layers, deformable biomarker attention (DBA) acts as an internal cross-modal modulation pathway rather than a third input modality, enabling sparse biomarker-guided spatial sampling from the 3D MRI feature grid. The framework was evaluated on ADNI data under strict subject-wise train, validation and test splits, so that repeated scans from the same individual never crossed partitions. On the held-out test set, the model achieved 95.68% accuracy, 95.39% macroprecision, 94.65% macrorecall and 95.01% macro\n                    <jats:italic>F<\/jats:italic>\n                    1 score, exceeding all comparison methods reproduced under the same protocol. Prediction uncertainty was higher for misclassified cases, and rejecting the most uncertain cases raised retained set accuracy to 98.31% at 85.03% coverage. Performance degraded gradually rather than catastrophically when clinical scores were with-held, falling to 93.20% accuracy with all five biomarkers imputed. These results indicate that within-encoder biomarker conditioning improves AD classification and yields uncertainty-aware predictions that may support decision-making in clinical research settings.\n                  <\/jats:p>","DOI":"10.1007\/s10278-026-02152-2","type":"journal-article","created":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T17:19:49Z","timestamp":1785777589000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Biomarker-Conditioned Vision Transformers with Deformable Biomarker Attention for Alzheimer\u2019s Disease Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0880-5466","authenticated-orcid":false,"given":"Mohammed G.","family":"Alsubaie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suhuai","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kamran","family":"Shaukat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weijia","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,3]]},"reference":[{"key":"2152_CR1","unstructured":"World Health Organization: World Failing to Address Dementia Challenge. WHO News Release. Accessed 2026\u201301\u201319 (2021). https:\/\/www.who.int\/news\/item\/ 02\u201309\u20132021-world-failing-to-address-dementia-challenge"},{"key":"2152_CR2","doi-asserted-by":"publisher","unstructured":"GBD 2019 Dementia Forecasting Collaborators: Estimation of the global preva-lence of dementia in 2019 and forecasted prevalence in 2050: An analysis for the Global Burden of Disease Study 2019. Lancet Public Health 7(2), 105\u2013125 (2022) https:\/\/doi.org\/10.1016\/S2468-2667(21)00249-8","DOI":"10.1016\/S2468-2667(21)00249-8"},{"key":"2152_CR3","unstructured":"World Health Organization: Dementia. WHO Fact Sheet. Last updated 31 Mar 2025; accessed 2026\u201301\u201319 (2025). https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/dementia"},{"issue":"4","key":"2152_CR4","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1016\/j.jalz.2018.02.018","volume":"14","author":"CRJ Jack","year":"2018","unstructured":"Jack, C.R.J., Bennett, D.A., Blennow, K., Carrillo, M.C., Dunn, B., Haeberlein, S.B., Holtzman, D.M., Jagust, W., Jessen, F., Karlawish, J., Liu, E., Molinuevo, J.L., Montine, T., Phelps, C., Rankin, K.P., Rowe, C.C., Scheltens, P., Siemers, E., Snyder, H.M., Sperling, R.A.: NIA-AA research framework: Toward a biological definition of alzheimer\u2019s disease. Alzheimers Dement 14(4), 535\u2013562 (2018) https:\/\/doi.org\/10.1016\/j.jalz.2018.02.018","journal-title":"Alzheimers Dement"},{"issue":"3","key":"2152_CR5","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.jalz.2011.03.003","volume":"7","author":"RA Sperling","year":"2011","unstructured":"Sperling, R.A., Aisen, P.S., Beckett, L.A., Bennett, D.A., Craft, S., Fagan, A.M., Iwatsubo, T., Jack, C.R.J., Kaye, J., Montine, T.J., Park, D.C., Reiman, E.M., Rowe, C.C., Siemers, E., Stern, Y., Yaffe, K., Carrillo, M.C., Thies, B., Morrison-Bogorad, M., Wagster, M.V., Phelps, C.H.: Toward defining the preclinical stages of alzheimer\u2019s disease: Recommendations from the National Institute on Aging-Alzheimer\u2019s Association workgroups on diagnostic guidelines. Alzheimers Dement 7(3), 280\u2013292 (2011) https:\/\/doi.org\/10.1016\/j.jalz.2011.03.003","journal-title":"Alzheimers Dement"},{"issue":"5","key":"2152_CR6","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.3390\/biomedicines11051423","volume":"11","author":"P Wei","year":"2023","unstructured":"Wei, P.: Ultra-early screening of cognitive decline due to Alzheimer\u2019s pathology. Biomedicines 11(5), 1423 (2023) https:\/\/doi.org\/10.3390\/biomedicines11051423","journal-title":"Biomedicines"},{"issue":"3","key":"2152_CR7","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1111\/j.1365-2796.2004.01388.x","volume":"256","author":"RC Petersen","year":"2004","unstructured":"Petersen, R.C.: Mild cognitive impairment as a diagnostic entity. J Intern Med 256(3), 183\u2013194 (2004) https:\/\/doi.org\/10.1111\/j.1365-2796.2004.01388.x","journal-title":"J Intern Med"},{"key":"2152_CR8","doi-asserted-by":"publisher","unstructured":"Sun, J., Han, J.-D.J., Chen, W.: Exploring the relationship among alzheimer\u2019s disease, aging and cognitive scores through a neuroimaging-based approach. Sci Rep 14, 27472 (2024) https:\/\/doi.org\/10.1038\/s41598-024-78712-9","DOI":"10.1038\/s41598-024-78712-9"},{"issue":"3","key":"2152_CR9","doi-asserted-by":"publisher","first-page":"1725","DOI":"10.1002\/alz.13565","volume":"20","author":"V Devanarayan","year":"2024","unstructured":"Devanarayan, V., Ye, Y., Charil, A., Andreozzi, E., Sachdev, P., Llano, D.A., Tian, L., Zhu, L., Hampel, H., Kramer, L., Dhadda, S., Irizarry, M., Alzheimer\u2019s Disease Neuroimaging Initiative: Predicting clinical progression trajectories of early Alzheimer\u2019s disease patients. Alzheimers Dement 20(3), 1725\u20131738 (2024) https:\/\/doi.org\/10.1002\/alz.13565","journal-title":"Alzheimers Dement"},{"issue":"2","key":"2152_CR10","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1038\/nrneurol.2009.215","volume":"6","author":"GB Frisoni","year":"2010","unstructured":"Frisoni, G.B., Fox, N.C., Jack, C.R., Scheltens, P., Thompson, P.M.: The clinical use of structural mri in alzheimer\u2019s disease. Nat Rev Neurol 6(2), 67\u201377 (2010) https:\/\/doi.org\/10.1038\/nrneurol.2009.215","journal-title":"Nat Rev Neurol"},{"issue":"4","key":"2152_CR11","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1002\/jmri.21049","volume":"27","author":"CRJ Jack","year":"2008","unstructured":"Jack, C.R.J., Bernstein, M.A., Fox, N.C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P.J., Whitwell, J.L., Ward, C., Dale, A.M., Felmlee, J.P., Gunter, J.L., Hill, D.L.G., 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.: The Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging 27(4), 685\u2013691 (2008) https:\/\/doi.org\/10.1002\/jmri.21049","journal-title":"J Magn Reson Imaging"},{"issue":"1","key":"2152_CR12","doi-asserted-by":"publisher","first-page":"14338","DOI":"10.1002\/alz.14338","volume":"21","author":"GD Rabinovici","year":"2025","unstructured":"Rabinovici, G.D., Knopman, D.S., Arbizu, J., Benzinger, T.L.S., Donohoe, K.J., Hansson, O., Herscovitch, P., Kuo, P.H., Lingler, J.H., Minoshima, S., Murray, M.E., Price, J.C., Salloway, S.P., Weber, C.J., Carrillo, M.C., Johnson, K.A.: Updated appropriate use criteria for amyloid and tau PET: A report from the Alzheimer\u2019s Association and Society of Nuclear Medicine and Molecular Imaging workgroup. Alzheimers Dement 21(1), 14338 (2025) https:\/\/doi.org\/10.1002\/alz.14338","journal-title":"Alzheimers Dement"},{"issue":"1","key":"2152_CR13","doi-asserted-by":"publisher","first-page":"12314","DOI":"10.1002\/dad2.12314","volume":"14","author":"FH Bouwman","year":"2022","unstructured":"Bouwman, F.H., Frisoni, G.B., Johnson, S.C., Chen, X., Engelborghs, S., Ikeuchi, T., Paquet, C., Ritchie, C., Bozeat, S., Quevenco, F.-C., Teunissen, C.E.: Clinical application of CSF biomarkers for alzheimer\u2019s disease: From rationale to ratios. Alzheimers Dement (Amst) 14(1), 12314 (2022) https:\/\/doi.org\/10.1002\/dad2.12314","journal-title":"Alzheimers Dement (Amst)"},{"key":"2152_CR14","doi-asserted-by":"publisher","unstructured":"Blennow, K., Zetterberg, H.: The past and the future of alzheimer\u2019s disease CSF biomarkers\u2014a journey toward validated biochemical tests covering the whole spectrum of molecular events. Front Neurosci 9, 345 (2015) https:\/\/doi.org\/10.3389\/fnins.2015.00345","DOI":"10.3389\/fnins.2015.00345"},{"issue":"3","key":"2152_CR15","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/0022-3956(75)90026-6","volume":"12","author":"MF Folstein","year":"1975","unstructured":"Folstein, M.F., Folstein, S.E., McHugh, P.R.: \u201cmini-mental state\u201d: A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12(3), 189\u2013198 (1975) https:\/\/doi.org\/10.1016\/0022-3956(75)90026-6","journal-title":"J Psychiatr Res"},{"issue":"1","key":"2152_CR16","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/0022-3956(82)90033-4","volume":"17","author":"JA Yesavage","year":"1983","unstructured":"Yesavage, J.A., Brink, T.L., Rose, T.L., Lum, O., Huang, V., Adey, M., Leirer, V.O.: Development and validation of a geriatric depression screening scale: A preliminary report. J Psychiatr Res 17(1), 37\u201349 (1983) https:\/\/doi.org\/10.1016\/0022-3956(82)90033-4","journal-title":"J Psychiatr Res"},{"issue":"11","key":"2152_CR17","doi-asserted-by":"publisher","first-page":"2412","DOI":"10.1212\/WNL.43.11.2412-a","volume":"43","author":"JC Morris","year":"1993","unstructured":"Morris, J.C.: The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology 43(11), 2412\u20132414 (1993) https:\/\/doi.org\/10.1212\/WNL.43.11.2412-a","journal-title":"Neurology"},{"issue":"3","key":"2152_CR18","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1093\/geronj\/37.3.323","volume":"37","author":"RI Pfeffer","year":"1982","unstructured":"Pfeffer, R.I., Kurosaki, T.T., Harrah, C.H., Chance, J.M., Filos, S.: Measurement of functional activities in older adults in the community. J Gerontol 37(3), 323\u2013329 (1982) https:\/\/doi.org\/10.1093\/geronj\/37.3.323","journal-title":"J Gerontol"},{"issue":"2","key":"2152_CR19","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1176\/jnp.12.2.233","volume":"12","author":"DI Kaufer","year":"2000","unstructured":"Kaufer, D.I., Cummings, J.L., Ketchel, P., Smith, V., MacMillan, A., Shelley, T., Lopez, O.L., DeKosky, S.T.: Validation of the NPI-Q, a brief clinical form of the Neuropsychiatric Inventory. J Neuropsychiatry Clin Neurosci 12(2), 233\u2013239 (2000) https:\/\/doi.org\/10.1176\/jnp.12.2.233","journal-title":"J Neuropsychiatry Clin Neurosci"},{"key":"2152_CR20","doi-asserted-by":"publisher","unstructured":"Acosta, J.N., Falcone, G.J., Rajpurkar, P., Topol, E.J.: Multimodal biomedical AI. Nat Med 28, 1773\u20131784 (2022) https:\/\/doi.org\/10.1038\/s41591-022-01981-2","DOI":"10.1038\/s41591-022-01981-2"},{"key":"2152_CR21","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: Deformable transformers for end-to-end object detection. In: International Conference on Learning Representations (ICLR) (2021). https:\/\/arxiv.org\/abs\/2010.04159"},{"key":"2152_CR22","doi-asserted-by":"publisher","unstructured":"Hosseini-Asl, E., Ghazal, M., Mahmoud, A., Aslantas, A., Shalaby, A., Casanova, M., Barnes, G., Gimel\u2019farb, G., Keynton, R., El-Baz, A.: Alzheimer\u2019s disease diagnostics by a 3D deeply supervised adaptable convolutional network. Front Biosci (Landmark Ed) 23(3), 584\u2013596 (2018) https:\/\/doi.org\/10.2741\/4606","DOI":"10.2741\/4606"},{"key":"2152_CR23","doi-asserted-by":"publisher","unstructured":"Korolev, S., Safiullin, A., Belyaev, M., Dodonova, Y.: Residual and plain con-volutional neural networks for 3D brain MRI classification. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, Australia, pp. 835\u2013838 (2017). https:\/\/doi.org\/10.1109\/ISBI.2017.7950647","DOI":"10.1109\/ISBI.2017.7950647"},{"key":"2152_CR24","first-page":"1571","volume":"2018","author":"C Yang","year":"2018","unstructured":"Yang, C., Rangarajan, A., Ranka, S.: Visual explanations from deep 3D convo-lutional neural networks for alzheimer\u2019s disease classification. AMIA Annu Symp Proc 2018, 1571\u20131580 (2018)","journal-title":"AMIA Annu Symp Proc"},{"key":"2152_CR25","doi-asserted-by":"publisher","unstructured":"Folego, G., Weiler, M., Casseb, R.F., Pires, R., Rocha, A., Alzheimer\u2019s Disease Neuroimaging Initiative: Alzheimer\u2019s disease detection through whole-brain 3D-CNN MRI. Front Bioeng Biotechnol 8, 534592 (2020) https:\/\/doi.org\/10.3389\/fbioe.2020.534592","DOI":"10.3389\/fbioe.2020.534592"},{"key":"2152_CR26","doi-asserted-by":"publisher","unstructured":"Wen, J., Thibeau-Sutre, E., Diaz-Melo, M., Samper-Gonz\u00b4alez, J., Routier, A., Bottani, S., Dormont, D., Durrleman, S., Burgos, N., Colliot, O., Alzheimer\u2019s Disease Neuroimaging Initiative, Australian Imaging Biomarkers and Lifestyle flagship study of ageing: Convolutional neural networks for classification of Alzheimer\u2019s disease: Overview and reproducible evaluation. Med Image Anal 63, 101694 (2020) https:\/\/doi.org\/10.1016\/j.media.2020.101694","DOI":"10.1016\/j.media.2020.101694"},{"issue":"1","key":"2152_CR27","doi-asserted-by":"publisher","first-page":"22544","DOI":"10.1038\/s41598-021-01681-w","volume":"11","author":"E Yagis","year":"2021","unstructured":"Yagis, E., Atnafu, S.W., Herrera, A., Marzi, C., Scheda, R., Giannelli, M., Tessa, C., Citi, L., Diciotti, S.: Effect of data leakage in brain MRI classification using 2D convolutional neural networks. Sci Rep 11(1), 22544 (2021) https:\/\/doi.org\/10.1038\/s41598-021-01681-w","journal-title":"Sci Rep"},{"key":"2152_CR28","doi-asserted-by":"publisher","unstructured":"Alarjani, M., Almuaibed, A.: Optimizing a 3D convolutional neural network to detect Alzheimer\u2019s disease based on MRI. PeerJ Comput Sci 11, 3129 (2025) https:\/\/doi.org\/10.7717\/peerj-cs.3129","DOI":"10.7717\/peerj-cs.3129"},{"issue":"21","key":"2152_CR29","doi-asserted-by":"publisher","first-page":"2709","DOI":"10.3390\/diagnostics15212709","volume":"15","author":"F Sheikh","year":"2025","unstructured":"Sheikh, F., Al Marouf, A., Rokne, J.G., Alhajj, R.: Lightweight deep learn-ing models with explainable AI for early Alzheimer\u2019s detection from standard MRI scans. Diagnostics (Basel) 15(21), 2709 (2025) https:\/\/doi.org\/10.3390\/diagnostics15212709","journal-title":"Diagnostics (Basel)"},{"key":"2152_CR30","doi-asserted-by":"publisher","unstructured":"Alsubaie, M.G., Luo, S., Shaukat, K., Zhang, W., Li, J.: A novel deep learning approach for Alzheimer\u2019s disease detection: Attention-driven convolutional neural networks with multi-activation fusion. AI 6(12), 324 (2025) https:\/\/doi.org\/10.3390\/ai6120324","DOI":"10.3390\/ai6120324"},{"key":"2152_CR31","doi-asserted-by":"publisher","unstructured":"Zhao, Z., Yeoh, P.S.Q., Zuo, X., Chuah, J.H., Chow, C.-O., Wu, X., Lai, K.W.: Vision transformer-equipped convolutional neural networks for automated Alzheimer\u2019s disease diagnosis using 3D MRI scans. Front Neurol 15, 1490829 (2024) https:\/\/doi.org\/10.3389\/fneur.2024.1490829","DOI":"10.3389\/fneur.2024.1490829"},{"key":"2152_CR32","doi-asserted-by":"publisher","unstructured":"Alsubaie, M.G., Luo, S., Shaukat, K., Zhang, W.: A novel Alzheimer\u2019s disease classification framework integrating 3D CNN with bidirectional feature pyramid network and vision transformers. IEEE Access 14, 15241\u201315259 (2026) https:\/\/doi.org\/10.1109\/ACCESS.2026.3658311","DOI":"10.1109\/ACCESS.2026.3658311"},{"issue":"1","key":"2152_CR33","doi-asserted-by":"publisher","first-page":"8996","DOI":"10.1038\/s41598-024-59578-3","volume":"14","author":"S Alp","year":"2024","unstructured":"Alp, S., Akan, T., Bhuiyan, M.S., Disbrow, E.A., Conrad, S.A., Vanchiere, J.A., Kevil, C.G., Bhuiyan, M.A.N.: Joint transformer architecture in brain 3D MRI classification: its application in Alzheimer\u2019s disease classification. Sci Rep 14(1), 8996 (2024) https:\/\/doi.org\/10.1038\/s41598-024-59578-3","journal-title":"Sci Rep"},{"key":"2152_CR34","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations (2021). https:\/\/arxiv.org\/abs\/2010.11929"},{"key":"2152_CR35","doi-asserted-by":"publisher","unstructured":"Shamshad, F., Khan, S., Zamir, S.W., Khan, M.H., Hayat, M., Khan, F.S., Fu, H.: Transformers in medical imaging: A survey. Med Image Anal 88, 102802 (2023) https:\/\/doi.org\/10.1016\/j.media.2023.102802","DOI":"10.1016\/j.media.2023.102802"},{"key":"2152_CR36","doi-asserted-by":"publisher","unstructured":"Hoang, G.M., Kim, U.-H., Kim, J.G.: Vision transformers for the prediction of mild cognitive impairment to Alzheimer\u2019s disease progression using mid-sagittal sMRI. Front Aging Neurosci 15, 1102869 (2023) https:\/\/doi.org\/10.3389\/fnagi.2023.1102869","DOI":"10.3389\/fnagi.2023.1102869"},{"issue":"1","key":"2152_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-025-33965-w","volume":"16","author":"F Huang","year":"2026","unstructured":"Huang, F., Zhu, P., Chen, N., Qiu, A.: A token efficient vision framework using patch residual transformer for Alzheimer\u2019s disease diagnosis. Sci Rep 16(1), 1\u201316 (2026) https:\/\/doi.org\/10.1038\/s41598-025-33965-w","journal-title":"Sci Rep"},{"key":"2152_CR38","doi-asserted-by":"publisher","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin trans-former: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"2152_CR39","doi-asserted-by":"publisher","unstructured":"Almansour, A.G.M., Alshomrani, F., Almutairi, A.T.M., Alalwany, E., Alshuhri, M.S., Alshaari, H., Alfahaid, A.: DA-ViT: Deformable attention vision trans-former for Alzheimer\u2019s disease classification from MRI scans. Comput Model Eng Sci 144(2), 2395\u20132418 (2025) https:\/\/doi.org\/10.32604\/cmes.2025.069661","DOI":"10.32604\/cmes.2025.069661"},{"issue":"3","key":"2152_CR40","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.: Multimodal classification of Alzheimer\u2019s disease and mild cognitive impairment. NeuroImage 55(3), 856\u2013867 (2011) https:\/\/doi.org\/10.1016\/j.neuroimage.2011.01.008","journal-title":"NeuroImage"},{"key":"2152_CR41","doi-asserted-by":"publisher","unstructured":"Liu, S., Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., Feng, D., Fulham, M.J.: Multi-modal neuroimaging feature learning for multi-class diagnosis of Alzheimer\u2019s disease. IEEE Trans Biomed Eng 62(4), 1132\u20131140 (2015) https:\/\/doi.org\/10.1109\/TBME.2014.2372011","DOI":"10.1109\/TBME.2014.2372011"},{"issue":"1","key":"2152_CR42","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) https:\/\/doi.org\/10.1038\/s41598-020-74399-w","journal-title":"Sci Rep"},{"issue":"12","key":"2152_CR43","doi-asserted-by":"publisher","first-page":"2014","DOI":"10.1093\/jamia\/ocac168","volume":"29","author":"M Golovanevsky","year":"2022","unstructured":"Golovanevsky, M., Eickhoff, C., Singh, R.: Multimodal attention-based deep learn-ing for Alzheimer\u2019s disease diagnosis. J Am Med Inform Assoc 29(12), 2014\u20132022 (2022) https:\/\/doi.org\/10.1093\/jamia\/ocac168","journal-title":"J Am Med Inform Assoc"},{"key":"2152_CR44","doi-asserted-by":"publisher","unstructured":"Qiu, S., Miller, M.I., Joshi, P.S., Lee, J.C., Xue, C., Ni, Y., Wang, Y., De Anda-Duran, I., Hwang, P.H., Cramer, J.A., Dwyer, B.C., Hao, H., Kaku, M.C., Kedar, S., Lee, P.H., Mian, A.Z., Murman, D.L., O\u2019Shea, S., Paul, A.B., Saint-Hilaire, M.H., Sartor, E.A., Saxena, A.R., Shih, L.C., Small, J.E., Smith, M.J., Swami-nathan, A., Takahashi, C.E., Taraschenko, O., You, H., Yuan, J., Zhou, Y., Zhu, S., Alosco, M.L., Mez, J., Stein, T.D., Poston, K.L., Au, R., Kolachalama, V.B.: Multimodal deep learning for alzheimer\u2019s disease dementia assessment. Nat Commun 13, 3404 (2022) https:\/\/doi.org\/10.1038\/s41467-022-31037-5","DOI":"10.1038\/s41467-022-31037-5"},{"key":"2152_CR45","doi-asserted-by":"publisher","unstructured":"Liu, L., Liu, S., Zhang, L., To, X.V., Nasrallah, F., Chandra, S.S.: Cascaded multi-modal mixing transformers for Alzheimer\u2019s disease classification with incomplete data. NeuroImage 277, 120267 (2023) https:\/\/doi.org\/10.1016\/j.neuroimage.2023.120267","DOI":"10.1016\/j.neuroimage.2023.120267"},{"key":"2152_CR46","doi-asserted-by":"publisher","unstructured":"Yu, Q., Ma, Q., Da, L., Li, J., Wang, M., Xu, A., Li, Z., Li, W.: A transformer-based unified multimodal framework for Alzheimer\u2019s disease assessment. Com-put Biol Med 180, 108979 (2024) https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108979","DOI":"10.1016\/j.compbiomed.2024.108979"},{"key":"2152_CR47","doi-asserted-by":"publisher","unstructured":"Duenias, D., Nichyporuk, B., Arbel, T., Riklin Raviv, T., Alzheimer\u2019s Disease Neuroimaging Initiative: HyperFusion: A hypernetwork approach to multimodal integration of tabular and medical imaging data for predictive modeling. Med Image Anal 102, 103503 (2025) https:\/\/doi.org\/10.1016\/j.media.2025.103503","DOI":"10.1016\/j.media.2025.103503"},{"issue":"1","key":"2152_CR48","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1109\/TMI.2025.3594364","volume":"45","author":"Q Chen","year":"2026","unstructured":"Chen, Q., Wang, J., Wang, G., Hong, Y.: HoloDx: Knowledge- and data-driven multimodal diagnosis of Alzheimer\u2019s disease. IEEE Trans Med Imaging 45(1), 206\u2013219 (2026) https:\/\/doi.org\/10.1109\/TMI.2025.3594364","journal-title":"IEEE Trans Med Imaging"},{"issue":"10","key":"2152_CR49","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.3390\/bioengineering12101107","volume":"12","author":"S Rahman","year":"2025","unstructured":"Rahman, S., Rahman, M.M., Bhatt, S., Sundararajan, R., Faezipour, M.: NeuroNet-AD: A multimodal deep learning framework for multiclass Alzheimer\u2019s disease diagnosis. Bioengineering (Basel) 12(10), 1107 (2025) https:\/\/doi.org\/10.3390\/bioengineering12101107","journal-title":"Bioengineering (Basel)"},{"key":"2152_CR50","doi-asserted-by":"publisher","unstructured":"Bekhet, S., Saad, N., Farag, S.: Alzheimer disease predicting from clinical and MRI data using DeepALZNET dual pathway framework. Sci Rep 15, 43250 (2025) https:\/\/doi.org\/10.1038\/s41598-025-28221-0","DOI":"10.1038\/s41598-025-28221-0"},{"key":"2152_CR51","doi-asserted-by":"publisher","unstructured":"Castro-Silva, J.A., Moreno Garc\u00b4\u0131a, M.N., Peluffo-Ord\u00b4on\u02dcez, D.H.: Multiple inputs and mixed data for Alzheimer\u2019s disease classification based on 3D vision transformer. Mathematics (Basel) 12(17), 2720 (2024) https:\/\/doi.org\/10.3390\/math12172720","DOI":"10.3390\/math12172720"},{"key":"2152_CR52","doi-asserted-by":"publisher","unstructured":"Perez, E., Strub, F., Vries, H., Dumoulin, V., Courville, A.: FiLM: Visual reason-ing with a general conditioning layer. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018). https:\/\/doi.org\/10.1609\/aaai.v32i1.11671","DOI":"10.1609\/aaai.v32i1.11671"},{"issue":"18","key":"2152_CR53","doi-asserted-by":"publisher","first-page":"2348","DOI":"10.3390\/diagnostics15182348","volume":"15","author":"VM Young","year":"2025","unstructured":"Young, V.M., Gates, S., Garcia, L.Y., Salardini, A.: Data leakage in deep learning for Alzheimer\u2019s disease diagnosis: A scoping review of methodological rigor and performance inflation. Diagnostics (Basel) 15(18), 2348 (2025) https:\/\/doi.org\/10.3390\/diagnostics15182348","journal-title":"Diagnostics (Basel)"},{"key":"2152_CR54","doi-asserted-by":"publisher","unstructured":"Varoquaux, G.: Cross-validation failure: Small sample sizes lead to large error bars. NeuroImage 180, 68\u201377 (2018) https:\/\/doi.org\/10.1016\/j.neuroimage.2017.06.061","DOI":"10.1016\/j.neuroimage.2017.06.061"},{"key":"2152_CR55","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In: Proceedings of the 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1050\u20131059 (2016). https:\/\/proceedings.mlr.press\/v48\/gal16.html"},{"key":"2152_CR56","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: Proceedings of the 34th International Conference on Machine Learn-ing. Proceedings of Machine Learning Research, vol. 70, pp. 1321\u20131330 (2017). https:\/\/proceedings.mlr.press\/v70\/guo17a.html"},{"key":"2152_CR57","doi-asserted-by":"publisher","unstructured":"Kurz, A., Hauser, K., Mehrtens, H.A., Krieghoff-Henning, E., Hekler, A., Kather, J.N., Fr\u00a8ohling, S., Kalle, C., Brinker, T.J.: Uncertainty estimation in medical image classification: Systematic review. JMIR Med Inform 10(8), 36427 (2022) https:\/\/doi.org\/10.2196\/36427","DOI":"10.2196\/36427"},{"issue":"1","key":"2152_CR58","doi-asserted-by":"publisher","first-page":"100003","DOI":"10.1016\/j.metrad.2023.100003","volume":"1","author":"K Zou","year":"2023","unstructured":"Zou, K., Chen, Z., Yuan, X., Shen, X., Wang, M., Fu, H.: A review of uncertainty estimation and its application in medical imaging. Meta Radiol 1(1), 100003 (2023) https:\/\/doi.org\/10.1016\/j.metrad.2023.100003","journal-title":"Meta Radiol"},{"issue":"11","key":"2152_CR59","doi-asserted-by":"publisher","first-page":"3153","DOI":"10.1049\/ipr2.12841","volume":"17","author":"H Chen","year":"2023","unstructured":"Chen, H., Guo, H., Xing, L., Chen, D., Yuan, T., Zhang, Y., Zhang, X.: Multi-modal predictive classification of alzheimer\u2019s disease based on attention-combined fusion network: Integrated neuroimaging modalities and medical examination data. IET Image Process 17(11), 3153\u20133164 (2023) https:\/\/doi.org\/10.1049\/ipr2.12841","journal-title":"IET Image Process"},{"key":"2152_CR60","doi-asserted-by":"publisher","unstructured":"Chen, Y., Zhu, S., Fang, Z., Liu, C., Zou, B., Wang, Y., Chang, S., Jia, F., Qin, F., Fan, J., Peng, Y., Wang, C.: Toward robust early detection of Alzheimer\u2019s disease via an integrated multimodal learning approach. In: 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1\u20135 (2025). https:\/\/doi.org\/10.1109\/ICASSP49660.2025.10888363","DOI":"10.1109\/ICASSP49660.2025.10888363"},{"issue":"2","key":"2152_CR61","doi-asserted-by":"publisher","first-page":"367","DOI":"10.3390\/electronics15020367","volume":"15","author":"T-A Chang","year":"2026","unstructured":"Chang, T.-A., Yu, C.-C., Wang, Y.-H., Lei, Z.-P., Chang, C.-H.: A hierarchical multi-modal fusion framework for alzheimer\u2019s disease classification using 3d mri and clinical biomarkers. Electronics (Basel) 15(2), 367 (2026) https:\/\/doi.org\/10.3390\/electronics15020367","journal-title":"Electronics (Basel)"},{"key":"2152_CR62","doi-asserted-by":"publisher","unstructured":"Isensee, F., Schell, M., Pflueger, I., Brugnara, G., Bonekamp, D., Neuberger, U., Wick, A., Schlemmer, H.-P., Heiland, S., Wick, W., Bendszus, M., Maier-Hein, K.H., Kickingereder, P.: Automated brain extraction of multisequence MRI using artificial neural networks. Hum Brain Mapp 40(17), 4952\u20134964 (2019) https:\/\/doi.org\/10.1002\/hbm.24750","DOI":"10.1002\/hbm.24750"},{"issue":"6","key":"2152_CR63","doi-asserted-by":"publisher","first-page":"5064","DOI":"10.1002\/mp.17767","volume":"52","author":"X Yang","year":"2025","unstructured":"Yang, X., Dang, X., Cai, J., Li, J., Wang, X., Heng, P.-A.: Temporal-multimodal consistency alignment for Alzheimer\u2019s cognitive assessment prediction. Med Phys 52(6), 5064\u20135080 (2025) https:\/\/doi.org\/10.1002\/mp.17767","journal-title":"Med Phys"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02152-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-026-02152-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02152-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T17:19:52Z","timestamp":1785777592000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-026-02152-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,3]]},"references-count":63,"alternative-id":["2152"],"URL":"https:\/\/doi.org\/10.1007\/s10278-026-02152-2","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,3]]},"assertion":[{"value":"16 April 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 July 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 July 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 August 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Deidentified data were obtained from the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI). ADNI participant data were collected under approved ethics protocols at participating sites, and written informed consent was obtained from all participants by the ADNI investigators as part of the original study. This study involved secondary analysis of anonymised data under the ADNI data use agreement. No new human data were collected for this research; therefore, no additional ethical approval was required.","order":1,"name":"Ethics","label":"Ethics Approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Informed consent was obtained from all participants by the ADNI investigators as part of the original study.","order":2,"name":"Ethics","label":"Consent to Participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":3,"name":"Ethics","label":"Consent for Publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}]}}