{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T01:40:00Z","timestamp":1763343600392,"version":"3.45.0"},"reference-count":61,"publisher":"Tech Science Press","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.064354","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T04:01:21Z","timestamp":1746676881000},"page":"1805-1838","source":"Crossref","is-referenced-by-count":1,"title":["Multimodal Convolutional Mixer for Mild Cognitive Impairment Detection"],"prefix":"10.32604","volume":"84","author":[{"given":"Ovidijus","family":"Grigas","sequence":"first","affiliation":[]},{"given":"Robertas","family":"Dama\u0161evi\u010dius","sequence":"additional","affiliation":[]},{"given":"Rytis","family":"Maskeli\u016bnas","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1016\/S0140-6736(06)68542-5","article-title":"Mild cognitive impairment","volume":"367","author":"Gauthier","year":"2006","journal-title":"The Lancet"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-023-00987-5","article-title":"Computerized cognitive training for memory functions in mild cognitive impairment or dementia: a systematic review and meta-analysis","volume":"7","author":"Chan","year":"2024","journal-title":"npj Digit Med"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"1666","DOI":"10.1007\/s00415-007-0610-z","article-title":"Longitudinal CSF isoprostane and MRI atrophy in the progression to AD","volume":"254","author":"Leon","year":"2007","journal-title":"J Neurol"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"302","DOI":"10.5755\/j01.itc.53.1.34536","article-title":"Dual attention aware octave convolution network for early-stage alzheimer\u2019s disease detection","volume":"53","author":"Rangaraju","year":"2024","journal-title":"Inf Technol Control"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"262","DOI":"10.5755\/j01.itc.53.1.34718","article-title":"Optimizing parkinson\u2019s disease diagnosis with multimodal data fusion techniques","volume":"53","author":"Karthigeyan","year":"2024","journal-title":"Inf Technol Control"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/S1474-4422(09)70299-6","article-title":"Hypothetical model of dynamic biomarkers of the Alzheimer\u2019s pathological cascade","volume":"9","author":"Jack","year":"2010","journal-title":"Lancet Neurol"},{"key":"ref7","first-page":"435","article-title":"Brain imaging of mild cognitive impairment and Alzheimer\u2019s disease","volume":"8","author":"Yin","year":"2013 Feb","journal-title":"Neural Regen Res"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"e046879","DOI":"10.1136\/bmjopen-2020-046879","article-title":"Multimodal measurement approach to identify individuals with mild cognitive impairment: study protocol for a cross-sectional trial","volume":"11","author":"Gr\u00e4ssler","year":"2021","journal-title":"BMJ Open"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"46278","DOI":"10.1109\/ACCESS.2024.3381862","article-title":"Multimodal 3D deep learning for early diagnosis of alzheimer\u2019s disease","volume":"12","author":"Kim","year":"2024","journal-title":"IEEE Access"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"105652","DOI":"10.1016\/j.bspc.2023.105652","article-title":"Pyramid-attentive GAN for multimodal brain image complementation in Alzheimer\u2019s disease classification","volume":"89","author":"Zhang","year":"2024 Mar","journal-title":"Biomed Signal Process Control"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"e23969","DOI":"10.1097\/MD.0000000000023969","article-title":"Clinical impact of 11C-Pittsburgh compound-B positron emission tomography in addition to magnetic resonance imaging and single-photon emission computed tomography on diagnosis of mild cognitive impairment to Alzheimer\u2019s disease","volume":"100","author":"Kitajima","year":"2021 Jan","journal-title":"Medicine"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"143","DOI":"10.3399\/bjgp18X695213","article-title":"Artificial intelligence in medicine: current trends and future possibilities","volume":"68","author":"Buch","year":"2018","journal-title":"Br J Gen Pract"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"4040","DOI":"10.1109\/JBHI.2023.3280823","article-title":"Improving alzheimer\u2019s disease diagnosis with multi-modal PET embedding features by a 3D multi-task MLP-mixer neural network","volume":"27","author":"Zhang","year":"2023","journal-title":"IEEE J Biomed Health Inform"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"105937","DOI":"10.1016\/j.compbiomed.2022.105937","article-title":"Deep networks for behavioral variant frontotemporal dementia identification from multiple acquisition sources","volume":"148","author":"Di Benedetto","year":"2022","journal-title":"Comput Biol Med"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"7351","DOI":"10.1109\/TNNLS.2023.3250490","article-title":"Multibranch CNN with MLP-mixer-based feature exploration for high-performance disease diagnosis","volume":"35","author":"Zhou","year":"2024","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref16","unstructured":"Trockman A, Kolter JZ. Patches are all you need? arXiv:2201.09792. 2022."},{"key":"ref17","doi-asserted-by":"crossref","first-page":"S36","DOI":"10.1016\/j.metabol.2017.01.011","article-title":"Artificial intelligence in medicine","volume":"69","author":"Hamet","year":"2017","journal-title":"Metabolism"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"e1312","DOI":"10.1002\/widm.1312","article-title":"Causability and explainability of artificial intelligence in medicine","volume":"9","author":"Holzinger","year":"2019","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"104312","DOI":"10.1016\/j.bspc.2022.104312","article-title":"Deep learning-based classification of healthy aging controls, mild cognitive impairment and Alzheimer\u2019s disease using fusion of MRI-PET imaging","volume":"80","author":"Subramanyam Rallabandi","year":"2023","journal-title":"Biomed Signal Process Control"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"108544","DOI":"10.1016\/j.jneumeth.2019.108544","article-title":"A Gaussian-based model for early detection of mild cognitive impairment using multimodal neuroimaging","volume":"333","author":"Forouzannezhad","year":"2020","journal-title":"J Neurosci Methods"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"155010","DOI":"10.1088\/1361-6560\/ac0e77","article-title":"Mild cognitive impairment classification using combined structural and diffusion imaging biomarkers","volume":"66","author":"Perez-Gonzalez","year":"2021","journal-title":"Phys Med Biol"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"206","DOI":"10.3389\/fnagi.2020.00206","article-title":"Identifying early mild cognitive impairment by multi-modality MRI-based deep learning","volume":"12","author":"Kang","year":"2020","journal-title":"Front Aging Neurosci"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"3153","DOI":"10.1049\/ipr2.12841","article-title":"Multimodal predictive classification of Alzheimer\u2019s disease based on attention-combined fusion network: integrated neuroimaging modalities and medical examination data","volume":"17","author":"Chen","year":"2023","journal-title":"IET Image Process"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/MMUL.2022.3156471","article-title":"Multimodal fusion-based deep learning network for effective diagnosis of alzheimer\u2019s disease","volume":"29","author":"Dwivedi","year":"2022","journal-title":"IEEE Multimed"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.3390\/brainsci13071045","article-title":"Pareto optimized adaptive learning with transposed convolution for image fusion alzheimer\u2019s disease classification","volume":"13","author":"Odusami","year":"2023","journal-title":"Brain Sci"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"e23158","DOI":"10.1002\/ima.23158","article-title":"Multimodal neuroimaging fusion for alzheimer\u2019s disease: an image colorization approach with mobile vision transformer","volume":"34","author":"Odusami","year":"2024","journal-title":"Int J Imaging Syst Technol"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"381","DOI":"10.3390\/brainsci14040381","article-title":"Positive effect of super-resolved structural magnetic resonance imaging for mild cognitive impairment detection","volume":"14","author":"Grigas","year":"2024","journal-title":"Brain Sci"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.3390\/jpm13101496","article-title":"Optimized convolutional fusion for multimodal neuroimaging in alzheimer\u2019s disease diagnosis: enhancing data integration and feature extraction","volume":"13","author":"Odusami","year":"2023","journal-title":"J Pers Med"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"e34402","DOI":"10.1016\/j.heliyon.2024.e34402","article-title":"Alzheimer\u2019s disease stage recognition from MRI and PET imaging data using Pareto-optimal quantum dynamic optimization","volume":"10","author":"Odusami","year":"2024","journal-title":"Heliyon"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"886619","DOI":"10.3389\/fpsyg.2022.886619","article-title":"Brain structural and functional changes in cognitive impairment due to alzheimer\u2019s disease","volume":"13","author":"\u00c1vila Villanueva","year":"2022","journal-title":"Front Psychol"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"1734","DOI":"10.1001\/archneur.64.12.1734","article-title":"Hypertension and the risk of mild cognitive impairment","volume":"64","author":"Reitz","year":"2007","journal-title":"Arch Neurol"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"1384","DOI":"10.1111\/jgs.17583","article-title":"Effect of intensive blood pressure control on subtypes of mild cognitive impairment and risk of progression from SPRINT study","volume":"70","author":"Gaussoin","year":"2021","journal-title":"J Am Geriatr Soc"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.jalz.2005.06.003","article-title":"Ways toward an early diagnosis in Alzheimer\u2019s disease: the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI)","volume":"1","author":"Mueller","year":"2005","journal-title":"Alzheimer\u2019s Dementia"},{"key":"ref34","unstructured":"Mayo Clinic. Alzheimer\u2019s Disease Research Center. [cited 2024 Dec 8]. Available from: https:\/\/www.mayo.edu\/research\/centers-programs\/alzheimers-disease-research-center\/research-activities\/mayo-clinic-study-aging\/overview."},{"key":"ref35","doi-asserted-by":"crossref","unstructured":"LaMontagne PJ, Benzinger TL, Morris JC, Keefe S, Hornbeck R, Xiong C, et al. OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and alzheimer disease. 2019. doi:10.1101\/2019.12.13.19014902.","DOI":"10.1101\/2019.12.13.19014902"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"S208","DOI":"10.1016\/j.neuroimage.2004.07.051","article-title":"Advances in functional and structural MR image analysis and implementation as FSL","volume":"23","author":"Smith","year":"2004","journal-title":"NeuroImage"},{"key":"ref37","unstructured":"Andrew Hoopes ZK Alexander Zsikla. Freesurfer: Neuroimaging Analysis and Visualization Suite. [cited 2024 Dec 8]. Available from: https:\/\/github.com\/freesurfer\/freesurfer."},{"key":"ref38","doi-asserted-by":"crossref","first-page":"119474","DOI":"10.1016\/j.neuroimage.2022.119474","article-title":"SynthStrip: skull-stripping for any brain image","volume":"260","author":"Hoopes","year":"2022","journal-title":"NeuroImage"},{"key":"ref39","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.neuroimage.2013.12.021","article-title":"Cortical surface-based analysis reduces bias and variance in kinetic modeling of brain PET data","volume":"92","author":"Greve","year":"2014","journal-title":"NeuroImage"},{"key":"ref40","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.neuroimage.2016.02.042","article-title":"Different partial volume correction methods lead to different conclusions: an 18F-FDG-PET study of aging","volume":"132","author":"Greve","year":"2016","journal-title":"NeuroImage"},{"key":"ref41","doi-asserted-by":"crossref","first-page":"20140119","DOI":"10.1259\/bjr.20140119","article-title":"Partial-volume effect correction in positron emission tomography brain scan image using super-resolution image reconstruction","volume":"88","author":"Meechai","year":"2015","journal-title":"British J Radiol"},{"key":"ref42","unstructured":"Yildirim N. Deep image fusion: multi-sensor image (infrared and visible) Fusion using deep learning framework, Principal Component Analysis, Discrete Wavelet Transform\u2014github.com. [cited 2024 Dec 27]. Available from: https:\/\/github.com\/nuriyeyldrm\/deep_image_fusion."},{"key":"ref43","unstructured":"Hendrycks D, Gimpel K. Gaussian error linear units (GELUs). arXiv:1606.08415. 2016."},{"key":"ref44","doi-asserted-by":"crossref","first-page":"4504","DOI":"10.1109\/TPAMI.2024.3355155","article-title":"B-Cos alignment for inherently interpretable CNNs and vision transformers","volume":"46","author":"B\u00f6hle","year":"2024","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref45","series-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"1800","article-title":"Xception: deep learning with depthwise separable convolutions","author":"Chollet","year":"2017 Jul"},{"key":"ref46","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai XH, Unterthiner T, et al. An image is worth 16 \u00d7 16 words: transformers for image recognition at scale. arXiv:2010.11929. 2020."},{"key":"ref47","series-title":"2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"2082","article-title":"Slide-transformer: hierarchical vision transformer with local self-attention","author":"Pan","year":"2023 Jun"},{"key":"ref48","doi-asserted-by":"crossref","unstructured":"Leem S, Seo H. Attention guided CAM: visual explanations of vision transformer guided by self-attention. arXiv:2402.04563. 2024.","DOI":"10.1609\/aaai.v38i4.28077"},{"key":"ref49","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: visual explanations from deep networks via gradient-based localization","volume":"128","author":"Selvaraju","year":"2019","journal-title":"Int J Comput Vis"},{"key":"ref50","unstructured":"Lundberg S, Lee SI. A unified approach to interpreting model predictions. arXiv:1705.07874. 2017."},{"key":"ref51","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. arXiv:1706.03762. 2017."},{"key":"ref52","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. arXiv:1512.03385. 2015.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref53","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, van der Maaten L, Weinberger KQ. Densely connected convolutional networks. arXiv:1608.06993. 2016.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref54","doi-asserted-by":"crossref","unstructured":"Qin D, Leichner C, Delakis M, Fornoni M, Luo S, Yang F, et al. MobileNetV4: universal models for the mobile ecosystem. arXiv:2404.10518. 2024.","DOI":"10.1007\/978-3-031-73661-2_5"},{"key":"ref55","doi-asserted-by":"crossref","DOI":"10.1001\/archneurol.2007.23","article-title":"Magnetic resonance imaging white matter hyperintensities and brain volume in the prediction of mild cognitive impairment and dementia","volume":"65","author":"Smith","year":"2008","journal-title":"Arch Neurol"},{"key":"ref56","doi-asserted-by":"crossref","first-page":"1114","DOI":"10.1016\/j.neurobiolaging.2007.10.008","article-title":"Brain structure and function related to cognitive reserve variables in normal aging, mild cognitive impairment and Alzheimer\u2019s disease","volume":"30","author":"Sol\u00e9-Padull\u00e9s","year":"2009","journal-title":"Neurobiol Aging"},{"key":"ref57","doi-asserted-by":"crossref","first-page":"e66367","DOI":"10.1371\/journal.pone.0066367","article-title":"MRI markers for mild cognitive impairment: comparisons between white matter integrity and gray matter volume measurements","volume":"8","author":"Zhang","year":"2013","journal-title":"PLoS One"},{"key":"ref58","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1007\/s11682-022-00659-0","article-title":"Volume, density, and thickness brain abnormalities in mild cognitive impairment: an ALE meta-analysis controlling for age and education","volume":"16","author":"Raine","year":"2022","journal-title":"Brain Imaging Behav"},{"key":"ref59","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1038\/s41398-022-02193-5","article-title":"Mapping the genetic architecture of cortical morphology through neuroimaging: progress and perspectives","volume":"12","author":"van der Meer","year":"2022","journal-title":"Transl Psychiatry"},{"key":"ref60","unstructured":"Tolstikhin I, Houlsby N, Kolesnikov A, Beyer L, Zhai XH, Unterthiner T, et al. MLP-Mixer: an all-MLP architecture for vision. arXiv:2105.01601. 2021."},{"key":"ref61","doi-asserted-by":"crossref","first-page":"109792","DOI":"10.1016\/j.knosys.2022.109792","article-title":"Multi-Scale MLP-Mixer for image classification","volume":"258","author":"Zhang","year":"2022","journal-title":"Knowl Based Syst"}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-84-1\/TSP_CMC_64354\/TSP_CMC_64354.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T01:35:26Z","timestamp":1763343326000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v84n1\/61759"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":61,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.064354","relation":{},"ISSN":["1546-2226"],"issn-type":[{"type":"electronic","value":"1546-2226"}],"subject":[],"published":{"date-parts":[[2025]]}}}