{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:26:28Z","timestamp":1778081188770,"version":"3.51.4"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T00:00:00Z","timestamp":1720483200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T00:00:00Z","timestamp":1720483200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Science and Technology Development Project of Liaoning Province of China","award":["2021JH6\/10500127"],"award-info":[{"award-number":["2021JH6\/10500127"]}]},{"name":"Science Research Project of Liaoning Department of Education of China","award":["LJKZ0008"],"award-info":[{"award-number":["LJKZ0008"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s10489-024-05663-z","type":"journal-article","created":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T06:01:50Z","timestamp":1720504910000},"page":"9067-9087","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A novel dual-branch Alzheimer\u2019s disease diagnostic model based on distinguishing atrophic patch localization"],"prefix":"10.1007","volume":"54","author":[{"given":"Yue","family":"Tu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shukuan","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianzhong","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuankuan","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yilin","family":"Zhuang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,9]]},"reference":[{"key":"5663_CR1","doi-asserted-by":"crossref","first-page":"109031","DOI":"10.1016\/j.patcog.2022.109031","volume":"133","author":"SQ Abbas","year":"2023","unstructured":"Abbas SQ, Chi L, Chen YPP (2023) Transformed domain convolutional neural network for alzheimer\u2019s disease diagnosis using structural mri. Pattern Recognit 133:109031","journal-title":"Pattern Recognit"},{"key":"5663_CR2","doi-asserted-by":"crossref","first-page":"104879","DOI":"10.1016\/j.compbiomed.2021.104879","volume":"138","author":"S Alinsaif","year":"2021","unstructured":"Alinsaif S, Lang J, Initiative ADN et al (2021) 3d shearlet-based descriptors combined with deep features for the classification of alzheimer\u2019s disease based on mri data. Comput Biol Med 138:104879","journal-title":"Comput Biol Med"},{"issue":"2","key":"5663_CR3","doi-asserted-by":"crossref","first-page":"3767","DOI":"10.1007\/s11042-023-15738-7","volume":"83","author":"DA Arafa","year":"2024","unstructured":"Arafa DA, Moustafa HED, Ali HA et al (2024) A deep learning framework for early diagnosis of alzheimer\u2019s disease on mri images. Multimed Tools Appl 83(2):3767\u20133799","journal-title":"Multimed Tools Appl"},{"issue":"6","key":"5663_CR4","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1006\/nimg.2000.0582","volume":"11","author":"J Ashburner","year":"2000","unstructured":"Ashburner J, Friston KJ (2000) Voxel-based morphometry-the methods. Neuroimage 11(6):805\u2013821","journal-title":"Neuroimage"},{"issue":"9533","key":"5663_CR5","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/S0140-6736(06)69113-7","volume":"368","author":"K Blennow","year":"2006","unstructured":"Blennow K, de Leon MJ, Zetterberg H (2006) Alzheimer\u2019s disease. The Lancet 368(9533):387\u2013403","journal-title":"The Lancet"},{"issue":"1","key":"5663_CR6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-016-0001-8","volume":"6","author":"J Chen","year":"2016","unstructured":"Chen J, Duan X, Shu H et al (2016) Differential contributions of subregions of medial temporal lobe to memory system in amnestic mild cognitive impairment: insights from fmri study. Sci Rep 6(1):1\u201314","journal-title":"Sci Rep"},{"key":"5663_CR7","doi-asserted-by":"crossref","first-page":"107944","DOI":"10.1016\/j.patcog.2021.107944","volume":"116","author":"Y Chen","year":"2021","unstructured":"Chen Y, Xia Y (2021) Iterative sparse and deep learning for accurate diagnosis of alzheimer\u2019s disease. Pattern Recognit 116:107944","journal-title":"Pattern Recognit"},{"key":"5663_CR8","doi-asserted-by":"crossref","first-page":"102585","DOI":"10.1016\/j.media.2022.102585","volume":"82","author":"BM Cobbinah","year":"2022","unstructured":"Cobbinah BM, Sorg C, Yang Q et al (2022) Reducing variations in multi-center alzheimer\u2019s disease classification with convolutional adversarial autoencoder. Med Image Anal 82:102585","journal-title":"Med Image Anal"},{"issue":"5","key":"5663_CR9","doi-asserted-by":"crossref","first-page":"2099","DOI":"10.1109\/JBHI.2018.2882392","volume":"23","author":"R Cui","year":"2018","unstructured":"Cui R, Liu M (2018) Hippocampus analysis by combination of 3-d densenet and shapes for alzheimer\u2019s disease diagnosis. IEEE J Biomed Health Inf 23(5):2099\u20132107","journal-title":"IEEE J Biomed Health Inf"},{"key":"5663_CR10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compmedimag.2019.01.005","volume":"73","author":"R Cui","year":"2019","unstructured":"Cui R, Liu M, Initiative ADN et al (2019) Rnn-based longitudinal analysis for diagnosis of alzheimer\u2019s disease. Comput Med Imaging Graph 73:1\u201310","journal-title":"Comput Med Imaging Graph"},{"key":"5663_CR11","doi-asserted-by":"crossref","first-page":"63605","DOI":"10.1109\/ACCESS.2019.2913847","volume":"7","author":"C Feng","year":"2019","unstructured":"Feng C, Elazab A, Yang P et al (2019) Deep learning framework for alzheimer\u2019s disease diagnosis via 3d-cnn and fsbi-lstm. IEEE Access 7:63605\u201363618","journal-title":"IEEE Access"},{"issue":"2","key":"5663_CR12","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","volume":"62","author":"B Fischl","year":"2012","unstructured":"Fischl B (2012) Freesurfer. Neuroimage 62(2):774\u2013781","journal-title":"Freesurfer. Neuroimage"},{"issue":"3\u20134","key":"5663_CR13","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1159\/000442941","volume":"41","author":"LM Fonseca","year":"2016","unstructured":"Fonseca LM, Yokomizo JE, Bottino CM et al (2016) Frontal lobe degeneration in adults with down syndrome and alzheimer\u2019s disease: a review. Dement Geriatr Cogn Disord 41(3\u20134):123\u2013136","journal-title":"Dement Geriatr Cogn Disord"},{"issue":"2","key":"5663_CR14","first-page":"81","volume":"10","author":"AL Foundas","year":"1997","unstructured":"Foundas AL, Leonard CM, Mahoney SM et al (1997) Atrophy of the hippocampus, parietal cortex, and insula in alzheimer\u2019s disease: a volumetric magnetic resonance imaging study. Cogn Behav Neurol 10(2):81\u201389","journal-title":"Cogn Behav Neurol"},{"issue":"1","key":"5663_CR15","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/JBHI.2021.3097721","volume":"26","author":"X Gao","year":"2021","unstructured":"Gao X, Shi F, Shen D et al (2021) Task-induced pyramid and attention gan for multimodal brain image imputation and classification in alzheimer\u2019s disease. IEEE J Biomed Health Inf 26(1):36\u201343","journal-title":"IEEE J Biomed Health Inf"},{"issue":"9518","key":"5663_CR16","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1016\/S0140-6736(06)68542-5","volume":"367","author":"S Gauthier","year":"2006","unstructured":"Gauthier S, Reisberg B, Zaudig M et al (2006) Mild cognitive impairment. The Lancet 367(9518):1262\u20131270","journal-title":"The Lancet"},{"key":"5663_CR17","doi-asserted-by":"crossref","unstructured":"Gorgolewski K, Burns CD, Madison C, et al (2011) Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Frontiers in Neuroinformatics p 13","DOI":"10.3389\/fninf.2011.00013"},{"issue":"1","key":"5663_CR18","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1176\/jnp.12.1.25","volume":"12","author":"S Holroyd","year":"2000","unstructured":"Holroyd S, Shepherd ML, Downs JH III (2000) Occipital atrophy is associated with visual hallucinations in alzheimer\u2019s disease. J Neuropsychiatry Clin Neurosci 12(1):25\u201328","journal-title":"J Neuropsychiatry Clin Neurosci"},{"key":"5663_CR19","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning, pmlr, pp 448\u2013456"},{"key":"5663_CR20","unstructured":"Janou\u0161ov\u00e1 E, Vounou M, Wolz R, et al (2012) Biomarker discovery for sparse classification of brain images in alzheimer\u2019s disease. Annals of the BMVA (2)"},{"issue":"2","key":"5663_CR21","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1006\/nimg.2002.1132","volume":"17","author":"M Jenkinson","year":"2002","unstructured":"Jenkinson M, Bannister P, Brady M et al (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17(2):825\u2013841","journal-title":"Neuroimage"},{"issue":"2","key":"5663_CR22","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1002\/hbm.22642","volume":"36","author":"B Jie","year":"2015","unstructured":"Jie B, Zhang D, Cheng B et al (2015) Manifold regularized multitask feature learning for multimodality disease classification. Hum Brain Mapp 36(2):489\u2013507","journal-title":"Hum Brain Mapp"},{"key":"5663_CR23","doi-asserted-by":"crossref","unstructured":"Karwath A, Hubrich M, Kramer S, et al (2017) Convolutional neural networks for the identification of regions of interest in pet scans: a study of representation learning for diagnosing alzheimer\u2019s disease. In: Artificial intelligence in medicine: 16th conference on artificial intelligence in medicine, AIME 2017, Vienna, Austria, June 21-24, 2017, Proceedings 16, Springer, pp 316\u2013321","DOI":"10.1007\/978-3-319-59758-4_36"},{"issue":"1","key":"5663_CR24","first-page":"e12334","volume":"14","author":"D Kerwin","year":"2022","unstructured":"Kerwin D, Abdelnour C, Caramelli P et al (2022) Alzheimer\u2019s disease diagnosis and management: perspectives from around the world. Alzheimer\u2019s & Dementia: Diagnosis, Assessment & Disease Monitoring 14(1):e12334","journal-title":"Alzheimer\u2019s & Dementia: Diagnosis, Assessment & Disease Monitoring"},{"key":"5663_CR25","doi-asserted-by":"crossref","unstructured":"Korolev S, Safiullin A, Belyaev M, et al (2017) Residual and plain convolutional neural networks for 3d brain mri classification. In: 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017), IEEE, pp 835\u2013838","DOI":"10.1109\/ISBI.2017.7950647"},{"issue":"5","key":"5663_CR26","doi-asserted-by":"crossref","first-page":"1610","DOI":"10.1109\/JBHI.2015.2429556","volume":"19","author":"F Li","year":"2015","unstructured":"Li F, Tran L, Thung KH et al (2015) A robust deep model for improved classification of ad\/mci patients. IEEE J Biomed Health Inf 19(5):1610\u20131616","journal-title":"IEEE J Biomed Health Inf"},{"key":"5663_CR27","doi-asserted-by":"crossref","first-page":"104571","DOI":"10.1016\/j.bspc.2023.104571","volume":"82","author":"H Li","year":"2023","unstructured":"Li H, Tan Y, Miao J et al (2023) Attention-based and micro designed efficientnetb2 for diagnosis of alzheimer\u2019s disease. Biomed Signal Process Control 82:104571","journal-title":"Biomed Signal Process Control"},{"issue":"4","key":"5663_CR28","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1109\/TPAMI.2018.2889096","volume":"42","author":"C Lian","year":"2018","unstructured":"Lian C, Liu M, Zhang J et al (2018) 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","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5663_CR29","doi-asserted-by":"crossref","first-page":"777","DOI":"10.3389\/fnins.2018.00777","volume":"12","author":"W Lin","year":"2018","unstructured":"Lin W, Tong T, Gao Q et al (2018) Convolutional neural networks-based mri image analysis for the alzheimer\u2019s disease prediction from mild cognitive impairment. Front Neurosci 12:777","journal-title":"Front Neurosci"},{"key":"5663_CR30","doi-asserted-by":"crossref","first-page":"646013","DOI":"10.3389\/fnins.2021.646013","volume":"15","author":"W Lin","year":"2021","unstructured":"Lin W, Lin W, Chen G et al (2021) Bidirectional mapping of brain mri and pet with 3d reversible gan for the diagnosis of alzheimer\u2019s disease. Front Neurosci 15:646013","journal-title":"Front Neurosci"},{"issue":"6","key":"5663_CR31","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1109\/TMI.2016.2515021","volume":"35","author":"M Liu","year":"2016","unstructured":"Liu M, Zhang D, Shen D (2016) Relationship induced multi-template learning for diagnosis of alzheimer\u2019s disease and mild cognitive impairment. IEEE Trans Med Imaging 35(6):1463\u20131474","journal-title":"IEEE Trans Med Imaging"},{"key":"5663_CR32","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s12021-018-9370-4","volume":"16","author":"M Liu","year":"2018","unstructured":"Liu M, Cheng D, Wang K et al (2018a) Multi-modality cascaded convolutional neural networks for alzheimer\u2019s disease diagnosis. Neuroinformatics 16:295\u2013308","journal-title":"Neuroinformatics"},{"key":"5663_CR33","doi-asserted-by":"crossref","first-page":"35","DOI":"10.3389\/fninf.2018.00035","volume":"12","author":"M Liu","year":"2018","unstructured":"Liu M, Cheng D, Yan W et al (2018b) Classification of alzheimer\u2019s disease by combination of convolutional and recurrent neural networks using fdg-pet images. Front Neuroinform 12:35","journal-title":"Front Neuroinform"},{"issue":"5","key":"5663_CR34","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1109\/TBME.2018.2869989","volume":"66","author":"M Liu","year":"2018","unstructured":"Liu M, Zhang J, Adeli E et al (2018c) Joint classification and regression via deep multi-task multi-channel learning for alzheimer\u2019s disease diagnosis. IEEE Trans Biomed Eng 66(5):1195\u20131206","journal-title":"IEEE Trans Biomed Eng"},{"key":"5663_CR35","doi-asserted-by":"crossref","first-page":"116459","DOI":"10.1016\/j.neuroimage.2019.116459","volume":"208","author":"M Liu","year":"2020","unstructured":"Liu M, Li F, Yan H et al (2020) A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in alzheimer\u2019s disease. Neuroimage 208:116459","journal-title":"Neuroimage"},{"issue":"4","key":"5663_CR36","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1016\/j.nic.2005.09.008","volume":"15","author":"SG Mueller","year":"2005","unstructured":"Mueller SG, Weiner MW, Thal LJ et al (2005) The alzheimer\u2019s disease neuroimaging initiative. Neuroimaging Clin 15(4):869\u2013877","journal-title":"Neuroimaging Clin"},{"key":"5663_CR37","unstructured":"Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp 807\u2013814"},{"issue":"6","key":"5663_CR38","doi-asserted-by":"crossref","first-page":"1632","DOI":"10.1109\/TMI.2021.3063150","volume":"40","author":"Z Ning","year":"2021","unstructured":"Ning Z, Xiao Q, Feng Q et al (2021) Relation-induced multi-modal shared representation learning for alzheimer\u2019s disease diagnosis. IEEE Trans Med Imaging 40(6):1632\u20131645","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"5663_CR39","doi-asserted-by":"crossref","first-page":"e32441","DOI":"10.1371\/journal.pone.0032441","volume":"7","author":"L O\u2019Dwyer","year":"2012","unstructured":"O\u2019Dwyer L, Lamberton F, Bokde AL et al (2012) Using support vector machines with multiple indices of diffusion for automated classification of mild cognitive impairment. PloS one 7(2):e32441","journal-title":"PloS one"},{"key":"5663_CR40","doi-asserted-by":"crossref","first-page":"102223","DOI":"10.1016\/j.bspc.2020.102223","volume":"63","author":"B Oltu","year":"2021","unstructured":"Oltu B, Ak\u015fahin MF, Kibaro\u011flu S (2021) A novel electroencephalography based approach for alzheimer\u2019s disease and mild cognitive impairment detection. Biomed Signal Process Control 63:102223","journal-title":"Biomed Signal Process Control"},{"key":"5663_CR41","doi-asserted-by":"crossref","unstructured":"Ortiz A, Munilla J, Gorriz JM et al (2016) Ensembles of deep learning architectures for the early diagnosis of the alzheimer\u2019s disease. Int J Neural Syst 26(07):1650025","DOI":"10.1142\/S0129065716500258"},{"key":"5663_CR42","doi-asserted-by":"crossref","unstructured":"Pan Y, Liu M, Lian C et al (2018) Synthesizing missing pet from mri with cycle-consistent generative adversarial networks for alzheimer\u2019s disease diagnosis. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part III 11, Springer, pp 455\u2013463","DOI":"10.1007\/978-3-030-00931-1_52"},{"key":"5663_CR43","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.arr.2016.01.002","volume":"30","author":"L Pini","year":"2016","unstructured":"Pini L, Pievani M, Bocchetta M et al (2016) Brain atrophy in alzheimer\u2019s disease and aging. Ageing Res Rev 30:25\u201348","journal-title":"Ageing Res Rev"},{"key":"5663_CR44","doi-asserted-by":"crossref","first-page":"101903","DOI":"10.1016\/j.bspc.2020.101903","volume":"59","author":"B Richhariya","year":"2020","unstructured":"Richhariya B, Tanveer M, Rashid AH et al (2020) Diagnosis of alzheimer\u2019s disease using universum support vector machine based recursive feature elimination (usvm-rfe). Biomed Signal Process Control 59:101903","journal-title":"Biomed Signal Process Control"},{"key":"5663_CR45","doi-asserted-by":"crossref","unstructured":"Robbins H, Monro S (1951) A stochastic approximation method. The Annals of Mathematical Statistics 22(3)","DOI":"10.1214\/aoms\/1177729586"},{"key":"5663_CR46","doi-asserted-by":"crossref","first-page":"116189","DOI":"10.1016\/j.neuroimage.2019.116189","volume":"206","author":"ET Rolls","year":"2020","unstructured":"Rolls ET, Huang CC, Lin CP et al (2020) Automated anatomical labelling atlas 3. Neuroimage 206:116189","journal-title":"Neuroimage"},{"issue":"10284","key":"5663_CR47","doi-asserted-by":"crossref","first-page":"1577","DOI":"10.1016\/S0140-6736(20)32205-4","volume":"397","author":"P Scheltens","year":"2021","unstructured":"Scheltens P, De Strooper B, Kivipelto M et al (2021) Alzheimer\u2019s disease. The Lancet 397(10284):1577\u20131590","journal-title":"The Lancet"},{"key":"5663_CR48","doi-asserted-by":"crossref","unstructured":"Sharma R, Goel T, Tanveer M, et al (2023) Deep learning based diagnosis and prognosis of alzheimer\u2019s disease: a comprehensive review. IEEE Transactions on Cognitive and Developmental Systems","DOI":"10.1109\/TCDS.2023.3254209"},{"key":"5663_CR49","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.bbr.2019.03.004","volume":"365","author":"J Sheng","year":"2019","unstructured":"Sheng J, Wang B, Zhang Q et al (2019) A novel joint hcpmmp method for automatically classifying alzheimer\u2019s and different stage mci patients. Behav Brain Res 365:210\u2013221","journal-title":"Behav Brain Res"},{"key":"5663_CR50","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.neuroimage.2014.06.077","volume":"101","author":"HI Suk","year":"2014","unstructured":"Suk HI, Lee SW, Shen D et al (2014a) Hierarchical feature representation and multimodal fusion with deep learning for ad\/mci diagnosis. NeuroImage 101:569\u2013582","journal-title":"NeuroImage"},{"key":"5663_CR51","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.neuroimage.2014.06.077","volume":"101","author":"HI Suk","year":"2014","unstructured":"Suk HI, Lee SW, Shen D et al (2014b) Hierarchical feature representation and multimodal fusion with deep learning for ad\/mci diagnosis. NeuroImage 101:569\u2013582","journal-title":"NeuroImage"},{"key":"5663_CR52","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.neuroimage.2016.01.005","volume":"129","author":"HI Suk","year":"2016","unstructured":"Suk HI, Wee CY, Lee SW et al (2016) State-space model with deep learning for functional dynamics estimation in resting-state fmri. NeuroImage 129:292\u2013307","journal-title":"NeuroImage"},{"issue":"2","key":"5663_CR53","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1002\/jmri.27568","volume":"54","author":"H Takao","year":"2021","unstructured":"Takao H, Amemiya S, Abe O et al (2021) Reliability of changes in brain volume determined by longitudinal voxel-based morphometry. J Magn Reson Imaging 54(2):609\u2013616","journal-title":"J Magn Reson Imaging"},{"key":"5663_CR54","doi-asserted-by":"crossref","first-page":"105901","DOI":"10.1016\/j.compbiomed.2022.105901","volume":"148","author":"Y Tu","year":"2022","unstructured":"Tu Y, Lin S, Qiao J et al (2022) Alzheimer\u2019s disease diagnosis via multimodal feature fusion. Comput Biol Med 148:105901","journal-title":"Comput Biol Med"},{"key":"5663_CR55","doi-asserted-by":"crossref","first-page":"105709","DOI":"10.1016\/j.bspc.2023.105709","volume":"89","author":"Y Tu","year":"2024","unstructured":"Tu Y, Lin S, Qiao J et al (2024) Multimodal fusion diagnosis of alzheimer\u2019s disease based on fdg-pet generation. Biomed Signal Process Control 89:105709","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"5663_CR56","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1006\/nimg.2001.0978","volume":"15","author":"N Tzourio-Mazoyer","year":"2002","unstructured":"Tzourio-Mazoyer N, Landeau B, Papathanassiou D et al (2002) Automated anatomical labeling of activations in spm using a macroscopic anatomical parcellation of the mni mri single-subject brain. Neuroimage 15(1):273\u2013289","journal-title":"Neuroimage"},{"key":"5663_CR57","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.neucom.2018.12.018","volume":"333","author":"H Wang","year":"2019","unstructured":"Wang H, Shen Y, Wang S et al (2019) Ensemble of 3d densely connected convolutional network for diagnosis of mild cognitive impairment and alzheimer\u2019s disease. Neurocomputing 333:145\u2013156","journal-title":"Neurocomputing"},{"key":"5663_CR58","doi-asserted-by":"crossref","unstructured":"Wang SH, Phillips P, Sui Y et al (2018) Classification of alzheimer\u2019s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. J Med Syst 42:1\u201311","DOI":"10.1007\/s10916-018-0932-7"},{"issue":"11","key":"5663_CR59","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.3390\/brainsci12111555","volume":"12","author":"Y Xiong","year":"2022","unstructured":"Xiong Y, Ye C, Chen Y et al (2022) Altered functional connectivity of basal ganglia in mild cognitive impairment and alzheimer\u2019s disease. Brain Sci 12(11):1555","journal-title":"Brain Sci"},{"key":"5663_CR60","doi-asserted-by":"crossref","first-page":"26157","DOI":"10.1109\/ACCESS.2019.2894530","volume":"7","author":"L Xu","year":"2019","unstructured":"Xu L, Yao Z, Li J et al (2019) Sparse feature learning with label information for alzheimer\u2019s disease classification based on magnetic resonance imaging. IEEE Access 7:26157\u201326167","journal-title":"IEEE Access"},{"key":"5663_CR61","doi-asserted-by":"crossref","unstructured":"Yang H, Xu H, Li Q, et al (2019) Study of brain morphology change in alzheimer\u2019s disease and amnestic mild cognitive impairment compared with normal controls. Gen Psychiatr 32(2)","DOI":"10.1136\/gpsych-2018-100005"},{"key":"5663_CR62","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.neuroscience.2019.05.014","volume":"414","author":"F Zhang","year":"2019","unstructured":"Zhang F, Tian S, Chen S et al (2019) Voxel-based morphometry: improving the diagnosis of alzheimer\u2019s disease based on an extreme learning machine method from the adni cohort. Neuroscience 414:273\u2013279","journal-title":"Neuroscience"},{"issue":"22","key":"5663_CR63","doi-asserted-by":"crossref","first-page":"7634","DOI":"10.3390\/s21227634","volume":"21","author":"P Zhang","year":"2021","unstructured":"Zhang P, Lin S, Qiao J et al (2021) Diagnosis of alzheimer\u2019s disease with ensemble learning classifier and 3d convolutional neural network. Sensors 21(22):7634","journal-title":"Sensors"},{"key":"5663_CR64","doi-asserted-by":"crossref","first-page":"109376","DOI":"10.1016\/j.jneumeth.2021.109376","volume":"365","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Teng Q, Liu Y et al (2022) Diagnosis of alzheimer\u2019s disease based on regional attention with smri gray matter slices. J Neurosci Methods 365:109376","journal-title":"J Neurosci Methods"},{"key":"5663_CR65","doi-asserted-by":"crossref","unstructured":"Zhu T, Cao C, Wang Z et al (2020) Anatomical landmarks and dag network learning for alzheimer\u2019s disease diagnosis. IEEE Access 8:206063\u2013206073","DOI":"10.1109\/ACCESS.2020.3037107"},{"issue":"9","key":"5663_CR66","doi-asserted-by":"crossref","first-page":"2354","DOI":"10.1109\/TMI.2021.3077079","volume":"40","author":"W Zhu","year":"2021","unstructured":"Zhu W, Sun L, Huang J et al (2021) Dual attention multi-instance deep learning for alzheimer\u2019s disease diagnosis with structural mri. IEEE Trans Med Imaging 40(9):2354\u20132366","journal-title":"IEEE Trans Med Imaging"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05663-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05663-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05663-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T13:08:33Z","timestamp":1723727313000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05663-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,9]]},"references-count":66,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["5663"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05663-z","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,9]]},"assertion":[{"value":"30 June 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing of Interest"}},{"value":"A benchmark dataset, Alzheimer\u2019s Disease Neuroimaging Initiative(ADNI), which was used in our work, has obtained the informed consent from the participants. We have obtained permission to use this dataset. More information can be found in the following link:  (accessed on 16 July 2023).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and Informed Consent for Data Used"}}]}}