{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T22:20:07Z","timestamp":1787091607707,"version":"build-2736575974"},"reference-count":54,"publisher":"IOP Publishing","issue":"9","license":[{"start":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T00:00:00Z","timestamp":1682467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T00:00:00Z","timestamp":1682467200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Guangdong Provincial Key Laboratory of Medical Image Processing","award":["No.2020B1212060039"],"award-info":[{"award-number":["No.2020B1212060039"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61671230"],"award-info":[{"award-number":["61671230"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"crossref","award":["2019A1515010417"],"award-info":[{"award-number":["2019A1515010417"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2023,5,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    . Mild cognitive impairment (MCI) is a precursor to Alzheimer\u2019s disease (AD) which is an irreversible progressive neurodegenerative disease and its early diagnosis and intervention are of great significance. Recently, many deep learning methods have demonstrated the advantages of multi-modal neuroimages in MCI identification task. However, previous studies frequently simply concatenate patch-level features for prediction without modeling the dependencies among local features. Also, many methods only focus on modality-sharable information or modality-specific features and ignore their incorporation. This work aims to address above-mentioned issues and construct a model for accurate MCI identification.\n                    <jats:italic>Approach<\/jats:italic>\n                    . In this paper, we propose a multi-level fusion network for MCI identification using multi-modal neuroimages, which consists of local representation learning and dependency-aware global representation learning stages. Specifically, for each patient, we first extract multi-pair of patches from multiple same position in multi-modal neuroimages. After that, in the local representation learning stage, multiple dual-channel sub-networks, each of which consists of two modality-specific feature extraction branches and three sine-cosine fusion modules, are constructed to learn local features that preserve modality-sharable and modality specific representations simultaneously. In the dependency-aware global representation learning stage, we further capture long-range dependencies among local representations and integrate them into global ones for MCI identification.\n                    <jats:italic>Main results<\/jats:italic>\n                    . Experiments on ADNI-1\/ADNI-2 datasets demonstrate the superior performance of the proposed method in MCI identification tasks (Accuracy: 0.802, sensitivity: 0.821, specificity: 0.767 in MCI diagnosis task; accuracy: 0.849, sensitivity: 0.841, specificity: 0.856 in MCI conversion task) when compared with state-of-the-art methods. The proposed classification model has demonstrated a promising potential to predict MCI conversion and identify the disease-related regions in the brain.\n                    <jats:italic>Significance<\/jats:italic>\n                    . We propose a multi-level fusion network for MCI identification using multi-modal neuroimage. The results on ADNI datasets have demonstrated its feasibility and superiority.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/accac8","type":"journal-article","created":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T18:41:21Z","timestamp":1680720081000},"page":"095018","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Multi-level fusion network for mild cognitive impairment identification using multi-modal neuroimages"],"prefix":"10.1088","volume":"68","author":[{"given":"Haozhe","family":"Xu","sequence":"first","affiliation":[{"name":"Southern Medical University"},{"name":"Southern Medical University"},{"name":"Southern Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengzhou","family":"Zhong","sequence":"additional","affiliation":[{"name":"Southern Medical University"},{"name":"Southern Medical University"},{"name":"Southern Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Southern Medical University"},{"name":"Southern Medical University"},{"name":"Southern Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2023,4,26]]},"reference":[{"key":"pmbaccac8bib1","doi-asserted-by":"publisher","first-page":"e05652","DOI":"10.1016\/j.heliyon.2020.e05652","type":"journal-article","article-title":"Improving Alzheimer's stage categorization with convolutional neural network using transfer learning and different magnetic resonance imaging modalities","volume":"6","author":"Aderghal","year":"2020","journal-title":"Heliyon"},{"key":"pmbaccac8bib2","doi-asserted-by":"publisher","first-page":"1877","DOI":"10.1093\/brain\/aww083","type":"journal-article","article-title":"Thalamic pathology and memory loss in early Alzheimer's disease: moving the focus from the medial temporal lobe to papez circuit","volume":"139","author":"Aggleton","year":"2016","journal-title":"Brain"},{"key":"pmbaccac8bib3","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101848","type":"journal-article","article-title":"Predicting the progression of mild cognitive impairment using machine learning: a systematic, quantitative and critical review","volume":"67","author":"Ansart","year":"2021","journal-title":"Med. Image Anal."},{"key":"pmbaccac8bib4","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1006\/nimg.2000.0582","type":"journal-article","article-title":"Voxel-based morphometry the methods","volume":"11","author":"Ashburner","year":"2000","journal-title":"NeuroImage"},{"key":"pmbaccac8bib5","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1016\/j.jalz.2016.03.001","type":"journal-article","article-title":"2016 Alzheimer's disease facts and figures","volume":"12","author":"Association","year":"2016","journal-title":"Alzheimer's & Dementia"},{"key":"pmbaccac8bib6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1175\/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2","type":"journal-article","article-title":"Verification of forecasts expressed in terms of probability","volume":"78","author":"Brier","year":"1950","journal-title":"Mon. Weather Rev."},{"key":"pmbaccac8bib7","doi-asserted-by":"publisher","first-page":"1805","DOI":"10.1109\/TBME.2015.2404809","type":"journal-article","article-title":"Domain transfer learning for mci conversion prediction","volume":"62","author":"Cheng","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"pmbaccac8bib8","doi-asserted-by":"publisher","first-page":"3736","DOI":"10.1016\/j.neuroimage.2011.10.080","type":"journal-article","article-title":"Simultaneous segmentation and grading of anatomical structures for patient's classification: application to Alzheimer's disease","volume":"59","author":"Coup\u00e9","year":"2012","journal-title":"NeuroImage"},{"key":"pmbaccac8bib9","doi-asserted-by":"publisher","first-page":"2099","DOI":"10.1109\/JBHI.2018.2882392","type":"journal-article","article-title":"Hippocampus analysis by combination of 3-d densenet and shapes for Alzheimer's disease diagnosis","volume":"23","author":"Cui","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"pmbaccac8bib10","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1109\/TBME.2012.2212278","type":"journal-article","article-title":"Machine learning-based method for personalized and cost-effective detection of Alzheimer's disease","volume":"60","author":"Escudero","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"pmbaccac8bib11","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1049\/iet-ipr.2019.0617","type":"journal-article","article-title":"Ensemble of deep convolutional neural networks based multi-modality images for Alzheimer's disease diagnosis","volume":"14","author":"Fang","year":"2020","journal-title":"IET Image Proc."},{"key":"pmbaccac8bib12","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1038\/nrneurol.2009.215","type":"journal-article","article-title":"The clinical use of structural mri in alzheimer disease","volume":"6","author":"Frisoni","year":"2010","journal-title":"Nat. Rev. Neurol."},{"key":"pmbaccac8bib13","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1109\/JBHI.2021.3097721","type":"journal-article","article-title":"Task-induced pyramid and attention gan for multimodal brain image imputation and classification in alzheimers disease","volume":"26","author":"Gao","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"pmbaccac8bib14","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101625","type":"journal-article","article-title":"Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer's disease","volume":"60","author":"Hao","year":"2020","journal-title":"Med. Image Anal."},{"key":"pmbaccac8bib15","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.1109\/ICCV.2015.123","type":"conference-proceedings","article-title":"Delving deep into rectifiers: surpassing human-level performance on imagenet classification","author":"He","year":"2015"},{"key":"pmbaccac8bib16","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1097\/00004728-199803000-00032","type":"journal-article","article-title":"Enhancement of Mr images using registration for signal averaging","volume":"22","author":"Holmes","year":"1998","journal-title":"J. Comput. Assist. Tomogr."},{"key":"pmbaccac8bib17","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1109\/ICIP.2016.7532332","type":"conference-proceedings","article-title":"Alzheimer's disease diagnostics by adaptation of 3d convolutional network","author":"Hosseini-Asl","year":"2016"},{"key":"pmbaccac8bib18","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1002\/jmri.21049","type":"journal-article","article-title":"The Alzheimer's disease neuroimaging initiative (adni): Mri methods","volume":"27","author":"Jack","year":"2008","journal-title":"J. Magn. Reson. Imaging"},{"key":"pmbaccac8bib19","doi-asserted-by":"publisher","first-page":"1519","DOI":"10.1212\/WNL.0b013e3181a2e864","type":"journal-article","article-title":"Risk of dementia in mci: combined effect of cerebrovascular disease, volumetric mri, and 1h mrs","volume":"72","author":"Kantarci","year":"2009","journal-title":"Neurology"},{"key":"pmbaccac8bib20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13550-021-00798-3","type":"journal-article","article-title":"Deep learning-based amyloid pet positivity classification model in the Alzheimer's disease continuum by using 2-[18f] fdg pet","volume":"11","author":"Kim","year":"2021","journal-title":"EJNMMI Res."},{"key":"pmbaccac8bib21","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1109\/RBME.2018.2796598","type":"journal-article","article-title":"Quantitative mri brain studies in mild cognitive impairment and Alzheimer's disease: a methodological review","volume":"11","author":"Leandrou","year":"2018","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"pmbaccac8bib22","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.jneumeth.2019.05.006","type":"journal-article","article-title":"A hybrid convolutional and recurrent neural network for hippocampus analysis in Alzheimer's disease","volume":"323","author":"Li","year":"2019","journal-title":"J. Neurosci. Methods"},{"key":"pmbaccac8bib23","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1109\/ISBI.2019.8759397","type":"conference-proceedings","article-title":"Early prediction of Alzheimer's disease dementia based on baseline hippocampal mri and 1-year follow-up cognitive measures using deep recurrent neural networks","author":"Li","year":"2019"},{"key":"pmbaccac8bib24","doi-asserted-by":"publisher","first-page":"1992","DOI":"10.1109\/TCYB.2020.3005859","type":"journal-article","article-title":"Attention-guided hybrid network for dementia diagnosis with structural Mr images","volume":"52","author":"Lian","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"pmbaccac8bib25","doi-asserted-by":"publisher","first-page":"880","DOI":"10.1109\/TPAMI.2018.2889096","type":"journal-article","article-title":"Hierarchical fully convolutional network for joint atrophy localization and Alzheimer's disease diagnosis using structural mri","volume":"42","author":"Lian","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"pmbaccac8bib26","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.media.2016.11.002","type":"journal-article","article-title":"View-aligned hypergraph learning for Alzheimer's disease diagnosis with incomplete multi-modality data","volume":"36","author":"Liu","year":"2017a","journal-title":"Med. Image Anal."},{"key":"pmbaccac8bib27","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/s12021-018-9370-4","type":"journal-article","article-title":"Multi-modality cascaded convolutional neural networks for Alzheimer's disease diagnosis","volume":"16","author":"Liu","year":"2018a","journal-title":"Neuroinformatics"},{"key":"pmbaccac8bib28","doi-asserted-by":"publisher","first-page":"1473","DOI":"10.1109\/TBME.2015.2496233","type":"journal-article","article-title":"Inherent structure-based multiview learning with multitemplate feature representation for Alzheimer's disease diagnosis","volume":"63","author":"Liu","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"pmbaccac8bib29","doi-asserted-by":"publisher","first-page":"1463","DOI":"10.1109\/TMI.2016.2515021","type":"journal-article","article-title":"Relationship induced multi-template learning for diagnosis of Alzheimer's disease and mild cognitive impairment","volume":"35","author":"Liu","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"pmbaccac8bib30","doi-asserted-by":"publisher","first-page":"1195","DOI":"10.1109\/TBME.2018.2869989","type":"journal-article","article-title":"Joint classification and regression via deep multi-task multi-channel learning for Alzheimer's disease diagnosis","volume":"66","author":"Liu","year":"2018b","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"pmbaccac8bib31","doi-asserted-by":"publisher","first-page":"3381","DOI":"10.1109\/TCYB.2019.2904186","type":"journal-article","article-title":"Weakly supervised deep learning for brain disease prognosis using mri and incomplete clinical scores","volume":"50","author":"Liu","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"pmbaccac8bib32","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.bbr.2017.01.037","type":"journal-article","article-title":"Decreased functional connectivity between the dorsal anterior cingulate cortex and lingual gyrus in Alzheimer's disease patients with depression","volume":"326","author":"Liu","year":"2017b","journal-title":"Behavioural Brain Res."},{"key":"pmbaccac8bib33","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102266","type":"journal-article","article-title":"Assessing clinical progression from subjective cognitive decline to mild cognitive impairment with incomplete multi-modal neuroimages","volume":"75","author":"Liu","year":"2022","journal-title":"Med. Image Anal."},{"key":"pmbaccac8bib34","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1111\/j.1600-0447.2008.01326.x","type":"journal-article","article-title":"Rate of progression of mild cognitive impairment to dementia\u2013meta-analysis of 41 robust inception cohort studies","volume":"119","author":"Mitchell","year":"2009","journal-title":"Acta Psychiatrica Scandinavica"},{"key":"pmbaccac8bib35","doi-asserted-by":"publisher","first-page":"1508","DOI":"10.1109\/TNNLS.2016.2520964","type":"journal-article","article-title":"Modeling disease progression via multisource multitask learners: a case study with Alzheimer's disease","volume":"28","author":"Nie","year":"2016","journal-title":"IEEE Trans Neural Netw. Learn. Syst."},{"key":"pmbaccac8bib36","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1109\/TMI.2020.3022591","type":"journal-article","article-title":"Multi-view separable pyramid network for ad prediction at mci stage by 18 f-fdg brain pet imaging","volume":"40","author":"Pan","year":"2020a","journal-title":"IEEE Trans. Med. Imaging"},{"key":"pmbaccac8bib37","doi-asserted-by":"publisher","first-page":"2965","DOI":"10.1109\/TMI.2020.2983085","type":"journal-article","article-title":"Spatially-constrained fisher representation for brain disease identification with incomplete multi-modal neuroimages","volume":"39","author":"Pan","year":"2020b","journal-title":"IEEE Trans. Med. Imaging"},{"key":"pmbaccac8bib38","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/978-3-030-00931-1_52","type":"conference-proceedings","article-title":"Synthesizing missing pet from mri with cycle-consistent generative adversarial networks for Alzheimer's disease diagnosis","author":"Pan","year":"2018"},{"key":"pmbaccac8bib39","doi-asserted-by":"publisher","author":"Penny","year":"2011","DOI":"10.1016\/B978-0-12-372560-8.X5000-1","type":"book"},{"key":"pmbaccac8bib40","doi-asserted-by":"publisher","DOI":"10.1117\/12.2294537","type":"conference-proceedings","article-title":"Deep-learning-based classification of fdg-pet data for Alzheimer's disease categories","volume":"10572","author":"Singh","year":"2017"},{"key":"pmbaccac8bib41","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/42.668698","type":"journal-article","article-title":"A nonparametric method for automatic correction of intensity nonuniformity in mri data","volume":"17","author":"Sled","year":"1998","journal-title":"IEEE Trans. Med. Imaging"},{"key":"pmbaccac8bib42","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.neuroimage.2019.01.031","type":"journal-article","article-title":"A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease","volume":"189","author":"Spasov","year":"2019","journal-title":"Neuroimage"},{"key":"pmbaccac8bib43","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1016\/j.neuroimage.2014.06.077","type":"journal-article","article-title":"Hierarchical feature representation and multimodal fusion with deep learning for ad\/mci diagnosis","volume":"101","author":"Suk","year":"2014","journal-title":"NeuroImage"},{"key":"pmbaccac8bib44","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1109\/TBME.2016.2549363","type":"journal-article","article-title":"A novel grading biomarker for the prediction of conversion from mild cognitive impairment to Alzheimer's disease","volume":"64","author":"Tong","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"pmbaccac8bib45","doi-asserted-by":"publisher","first-page":"6000","DOI":"10.5555\/3295222.3295349","type":"journal-article","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"pmbaccac8bib46","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1109\/TMI.2006.887380","type":"journal-article","article-title":"Large deformation diffeomorphism and momentum based hippocampal shape discrimination in dementia of the alzheimer type","volume":"26","author":"Wang","year":"2007","journal-title":"IEEE Trans. Med. Imaging"},{"key":"pmbaccac8bib47","doi-asserted-by":"publisher","first-page":"7794","DOI":"10.1109\/CVPR.2018.00813","type":"conference-proceedings","article-title":"Non-local neural networks","author":"Wang","year":"2018"},{"key":"pmbaccac8bib48","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1016\/j.neuroimage.2011.01.008","type":"journal-article","article-title":"Multimodal classification of Alzheimer's disease and mild cognitive impairment","volume":"55","author":"Zhang","year":"2011","journal-title":"NeuroImage"},{"key":"pmbaccac8bib49","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.neucom.2019.04.093","type":"journal-article","article-title":"Multi-modal deep learning model for auxiliary diagnosis of Alzheimer's disease","volume":"361","author":"Zhang","year":"2019b","journal-title":"Neurocomputing"},{"key":"pmbaccac8bib50","doi-asserted-by":"publisher","first-page":"2524","DOI":"10.1109\/TMI.2016.2582386","type":"journal-article","article-title":"Detecting anatomical landmarks for fast Alzheimer's disease diagnosis","volume":"35","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"pmbaccac8bib51","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102082","type":"journal-article","article-title":"Deep fusion of brain structure-function in mild cognitive impairment","volume":"72","author":"Zhang","year":"2021","journal-title":"Med. Image Anal."},{"key":"pmbaccac8bib52","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2020.108795","type":"journal-article","article-title":"Multi-modal neuroimaging feature fusion for diagnosis of Alzheimer's disease","volume":"341","author":"Zhang","year":"2020","journal-title":"J. Neurosci. Methods"},{"key":"pmbaccac8bib53","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1016\/j.patcog.2018.12.001","type":"journal-article","article-title":"Strength and similarity guided group-level brain functional network construction for mci diagnosis","volume":"88","author":"Zhang","year":"2019a","journal-title":"Pattern Recognit."},{"key":"pmbaccac8bib54","doi-asserted-by":"publisher","first-page":"2411","DOI":"10.1109\/TMI.2019.2913158","type":"journal-article","article-title":"Latent representation learning for Alzheimer's disease diagnosis with incomplete multi-modality neuroimaging and genetic data","volume":"38","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Physics in Medicine &amp; Biology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/accac8","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/accac8\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/accac8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/accac8\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/accac8\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/accac8\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/accac8\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/accac8\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T16:44:45Z","timestamp":1778777085000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/accac8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,26]]},"references-count":54,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,4,26]]},"published-print":{"date-parts":[[2023,5,7]]}},"URL":"https:\/\/doi.org\/10.1088\/1361-6560\/accac8","relation":{},"ISSN":["0031-9155","1361-6560"],"issn-type":[{"value":"0031-9155","type":"print"},{"value":"1361-6560","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,26]]},"assertion":[{"value":"Multi-level fusion network for mild cognitive impairment identification using multi-modal neuroimages","name":"article_title","label":"Article Title"},{"value":"Physics in Medicine & Biology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2023 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2022-08-22","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2023-04-05","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2023-04-26","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}