{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T12:14:40Z","timestamp":1780056880428,"version":"3.54.0"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T00:00:00Z","timestamp":1753920000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T00:00:00Z","timestamp":1753920000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"DOI":"10.1186\/s12880-025-01836-5","type":"journal-article","created":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T18:25:55Z","timestamp":1753986355000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["TA-SSM net: tri-directional attention and structured state-space model for enhanced MRI-Based diagnosis of Alzheimer\u2019s disease and mild cognitive impairment"],"prefix":"10.1186","volume":"25","author":[{"given":"Sichen","family":"Bao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengbo","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lifen","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiuyuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Lyu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,31]]},"reference":[{"issue":"11","key":"1836_CR1","doi-asserted-by":"publisher","first-page":"5289","DOI":"10.1109\/JBHI.2021.3066832","volume":"26","author":"X Zhang","year":"2021","unstructured":"Zhang X, Han L, Zhu W, Sun L, Zhang D. An explainable 3d residual self-attention deep neural network for joint atrophy localization and alzheimer\u2019s disease diagnosis using structural mri. IEEE J Biomed And Health Inf. 2021;26(11):5289\u201397.","journal-title":"IEEE J Biomed And Health Inf"},{"key":"1836_CR2","doi-asserted-by":"crossref","unstructured":"Petersen RC. Mild cognitive impairment. CONTINUUM: Lifelong learning in neurology. 2016;22(2):404\u201318.","DOI":"10.1212\/CON.0000000000000313"},{"issue":"2","key":"1836_CR3","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1007\/s40120-022-00338-8","volume":"11","author":"AAT Monfared","year":"2022","unstructured":"Monfared AAT, Byrnes MJ, White LA, Zhang Q. Alzheimer\u2019s disease: Epidemiology and clinical progression. Neurol And Ther. 2022;11(2):553\u201369.","journal-title":"Neurol And Ther"},{"issue":"3","key":"1836_CR4","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1016\/S1053-8119(03)00168-X","volume":"19","author":"C Huang","year":"2003","unstructured":"Huang C, Wahlund L-O, Almkvist O, Elehu D, Svensson L, Jonsson T, Winblad B, Julin P. Voxel-and voi-based analysis of spect cbf in relation to clinical and psychological heterogeneity of mild cognitive impairment. Neuroimage. 2003;19(3):1137\u201344.","journal-title":"Neuroimage"},{"issue":"2","key":"1836_CR5","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1038\/nrneurol.2009.215","volume":"6","author":"GB Frisoni","year":"2010","unstructured":"Frisoni GB, Fox NC, Jack CR Jr, Scheltens P, Thompson PM. The clinical use of structural mri in alzheimer disease. Nat Rev Neurol. 2010;6(2):67\u201377.","journal-title":"Nat Rev Neurol"},{"key":"1836_CR6","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1016\/j.neuroimage.2017.03.057","volume":"155","author":"S Rathore","year":"2017","unstructured":"Rathore S, Habes M, Iftikhar MA, Shacklett A, Davatzikos C. A review on neuroimaging-based classification studies and associated feature extraction methods for alzheimer\u2019s disease and its prodromal stages. NeuroImage. 2017;155:530\u201348.","journal-title":"NeuroImage"},{"key":"1836_CR7","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.compmedimag.2015.04.007","volume":"44","author":"OB Ahmed","year":"2015","unstructured":"Ahmed OB, Mizotin M, Benois-Pineau J, Allard M, Catheline G, Amar CB, et al. Alzheimer\u2019s disease diagnosis on structural mr images using circular harmonic functions descriptors on hippocampus and posterior cingulate cortex. Alzheimer\u2019s Dis Neuroimag Initiative, Computerized Med Imag Graphics. 2015;44:13\u201325.","journal-title":"Computerized Med Imag Graphics"},{"key":"1836_CR8","doi-asserted-by":"publisher","first-page":"108464","DOI":"10.1016\/j.cmpb.2024.108464","volume":"257","author":"R Agarwal","year":"2024","unstructured":"Agarwal R, Ghosal P, Sadhu AK, Murmu N, Nandi D. Multi-scale dual-channel feature embedding decoder for biomedical image segmentation. Comput Met Programs Biomed. 2024;257:108464.","journal-title":"Comput Met Programs Biomed"},{"key":"1836_CR9","doi-asserted-by":"publisher","first-page":"109752","DOI":"10.1016\/j.compbiomed.2025.109752","volume":"187","author":"A Chowdhury","year":"2025","unstructured":"Chowdhury A, Lodh A, Agarwal R, Garai R, Nandi A, Murmu N, Banerjee S, Nandi D. Rim learning framework based on ts-gan: A new paradigm of automated glaucoma screening from fundus images. Comput Biol Med. 2025;187:109752.","journal-title":"Comput Biol Med"},{"key":"1836_CR10","doi-asserted-by":"crossref","unstructured":"Agarwal R, Chowdhury A, Chatterjee RK, Chel H, Murmu C, Murmu N, Nandi D. Deep quasi-recurrent self-attention with dual encoder-decoder in biomedical ct image segmentation. IEEE Journal of Biomedical and Health Informatics. 2024.","DOI":"10.1109\/JBHI.2024.3447689"},{"key":"1836_CR11","doi-asserted-by":"crossref","unstructured":"Agarwal R, Ghosal P, Murmu N, Nandi D. Spiking neural network in computer vision: Techniques, tools and trends. International Conference on Advanced Computational and Communication Paradigms. Springer; 2023:201\u201309.","DOI":"10.1007\/978-981-99-4284-8_16"},{"issue":"12","key":"1836_CR12","doi-asserted-by":"publisher","first-page":"2633","DOI":"10.3390\/math11122633","volume":"11","author":"T Ghosh","year":"2023","unstructured":"Ghosh T, Palash MIA, Yousuf MA, Hamid MA, Monowar MM, Alassafi MO. A robust distributed deep learning approach to detect alzheimer\u2019s disease from mri images. Mathematics. 2023;11(12):2633.","journal-title":"Mathematics"},{"key":"1836_CR13","doi-asserted-by":"crossref","unstructured":"Lyu Y, Xiaowei Y, Zhu D, Zhang L. Classification of alzheimer\u2019s disease via vision transformer: Classification of alzheimer\u2019s disease via vision transformer. Proceedings of the 15th international conference on PErvasive technologies related to assistive environments. 2022:463\u201368.","DOI":"10.1145\/3529190.3534754"},{"issue":"6","key":"1836_CR14","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.1093\/brain\/awaa137","volume":"143","author":"S Qiu","year":"2020","unstructured":"Qiu S, Joshi PS, Miller MI, Xue C, Zhou X, Karjadi C, Chang GH, Joshi AS, Dwyer B, Zhu S, et al. Development and validation of an interpretable deep learning framework for alzheimer\u2019s disease classification. Brain. 2020;143(6):1920\u201333.","journal-title":"Brain"},{"key":"1836_CR15","unstructured":"Liu M, Zhang D, Yap P-T, Shen D. Hierarchical ensemble of multi-level classifiers for diagnosis of alzheimer\u2019s disease. In Machine learning in medical imaging: Third international workshop, MLMI 2012, held in conjunction with MICCAI 2012, Nice, France, Springer. October 1, 2012; Revised Selected Papers 3, 27\u201335."},{"issue":"12","key":"1836_CR16","doi-asserted-by":"publisher","first-page":"2524","DOI":"10.1109\/TMI.2016.2582386","volume":"35","author":"J Zhang","year":"2016","unstructured":"Zhang J, Gao Y, Gao Y, Munsell BC, Shen D. Detecting anatomical landmarks for fast alzheimer\u2019s disease diagnosis. IEEE Trans Med Imag. 2016;35(12):2524\u201333.","journal-title":"IEEE Trans Med Imag"},{"key":"1836_CR17","doi-asserted-by":"publisher","first-page":"103032","DOI":"10.1016\/j.media.2023.103032","volume":"91","author":"C Wang","year":"2024","unstructured":"Wang C, Piao S, Huang Z, Gao Q, Zhang J, Yuxin L, Shan H. Alzheimer\u2019s disease Neuroimaging initiative, et al. Joint learning framework of cross-modal synthesis and diagnosis for alzheimer\u2019s disease by mining underlying shared modality information. Med Image Anal. 2024;91:103032.","journal-title":"Med Image Anal"},{"key":"1836_CR18","unstructured":"Lozupone G, Bria A, Fontanella F, Meijer FJ, De Stefano C. Axial: Attention-based explainability for interpretable alzheimer\u2019s localized diagnosis using 2d cnns on 3d mri brain scans. 2024. arXiv preprint arXiv:2407.02418."},{"key":"1836_CR19","doi-asserted-by":"publisher","first-page":"1249","DOI":"10.1007\/s11042-014-2123-y","volume":"74","author":"OB Ahmed","year":"2015","unstructured":"Ahmed OB, Benois-Pineau J, Allard M, Amar CB. Gw\u00e9na\u00eblle Catheline, and Alzheimer\u2019s disease neuroimaging initiative. Classification of alzheimer\u2019s disease subjects from mri using hippocampal visual features. Multimedia Tools Appl. 2015;74:1249\u201366.","journal-title":"Multimedia Tools Appl"},{"key":"1836_CR20","doi-asserted-by":"crossref","unstructured":"Aderghal K, Afdel K, Benois-Pineau J, Catheline G. Improving alzheimer\u2019s stage categorization with convolutional neural network using transfer learning and different magnetic resonance imaging modalities. Heliyon. 2020;6(12).","DOI":"10.1016\/j.heliyon.2020.e05652"},{"issue":"2","key":"1836_CR21","doi-asserted-by":"publisher","first-page":"766","DOI":"10.1016\/j.neuroimage.2010.06.013","volume":"56","author":"R Cuingnet","year":"2011","unstructured":"Cuingnet R, Gerardin E, Tessieras J, Auzias G, Leh\u00e9ricy S, Habert M-O, Chupin M, Benali H, Colliot O, et al. Automatic classification of patients with alzheimer\u2019s disease from structural mri: a comparison of ten methods using the adni database. Alzheimer\u2019s Dis Neuroimag Initiative, Neuroimage. 2011;56(2):766\u201381","journal-title":"neuroimage"},{"issue":"1","key":"1836_CR22","first-page":"5485080","volume":"2017","author":"RK Lama","year":"2017","unstructured":"Lama RK, Gwak J, Park J-S, Lee S-W. Diagnosis of alzheimer\u2019s disease based on structural mri images using a regularized extreme learning machine and pca features. J Healthc Eng. 2017;2017(1):5485080.","journal-title":"J Healthc Eng"},{"key":"1836_CR23","doi-asserted-by":"publisher","first-page":"73373","DOI":"10.1109\/ACCESS.2019.2920011","volume":"7","author":"S Ahmed","year":"2019","unstructured":"Ahmed S, Choi KY, Lee JJ, Kim BC, Kwon G-R, Lee KH, Jung HY. Ensembles of patch-based classifiers for diagnosis of alzheimer diseases. IEEE Access. 2019;7:73373\u201383.","journal-title":"IEEE Access"},{"key":"1836_CR24","doi-asserted-by":"publisher","first-page":"1576931","DOI":"10.3389\/fnins.2025.1576931","volume":"19","author":"Z Ren","year":"2025","unstructured":"Ren Z, Zhou M, Shakil S, Tong RK-Y. Alzheimer\u2019s disease recognition via long-range state space model using multi-modal brain images. Front Neurosci. 2025;19:1576931.","journal-title":"Front Neurosci"},{"issue":"9","key":"1836_CR25","doi-asserted-by":"publisher","first-page":"2354","DOI":"10.1109\/TMI.2021.3077079","volume":"40","author":"W Zhu","year":"2021","unstructured":"Zhu W, Sun L, Huang J, Han L, Zhang D. Dual attention multi-instance deep learning for alzheimer\u2019s disease diagnosis with structural mri. IEEE Trans Med Imag. 2021;40(9):2354\u201366.","journal-title":"IEEE Trans Med Imag"},{"key":"1836_CR26","doi-asserted-by":"publisher","first-page":"e2056","DOI":"10.7717\/peerj-cs.2056","volume":"10","author":"T Wang","year":"2024","unstructured":"Wang T, Ding Z, Yang X, Chen Y, Liu Y, Kong X, Sun Y. Detection of mild cognitive impairment based on attention mechanism and parallel dilated convolution. PeerJ Comput Sci. 2024;10:e2056.","journal-title":"PeerJ Comput Sci"},{"key":"1836_CR27","unstructured":"Alexey D. An image is worth 16x16 words: Transformers for image recognition at scale. 2020. arXiv preprint arXiv: 2010.11929."},{"key":"1836_CR28","doi-asserted-by":"crossref","unstructured":"Wang Y, Nie J, Yap P-T, Shi F, Guo L, Shen D. Robust deformable-surface-based skull-stripping for large-scale studies. Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2011: 14th International Conference. Proceedings, Part III. Toronto, Canada: Springer; September 18\u201322 2011;14:635\u2013642.","DOI":"10.1007\/978-3-642-23626-6_78"},{"key":"1836_CR29","doi-asserted-by":"publisher","first-page":"109376","DOI":"10.1016\/j.jneumeth.2021.109376","volume":"365","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Teng Q, Liu Y, Liu Y, Xiaohai H. Diagnosis of alzheimer\u2019s disease based on regional attention with smri gray matter slices. J Neurosci Methods. 2022;365:109376.","journal-title":"J Neurosci Methods"},{"key":"1836_CR30","doi-asserted-by":"crossref","unstructured":"Heising L, Angelopoulos S. Operationalising fairness in medical ai adoption: Detection of early alzheimer\u2019s disease with 2d cnn. BMJ Health & Care Inf. 2022;29(1).","DOI":"10.1136\/bmjhci-2021-100485"},{"key":"1836_CR31","doi-asserted-by":"publisher","first-page":"110546","DOI":"10.1016\/j.knosys.2023.110546","volume":"270","author":"J Zhang","year":"2023","unstructured":"Zhang J, Xiaohai H, Qing L, Chen X, Liu Y, Chen H. Multi-relation graph convolutional network for alzheimer\u2019s disease diagnosis using structural mri. Knowl-Based Syst. 2023;270:110546.","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"1836_CR32","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1109\/JBHI.2021.3097721","volume":"26","author":"X Gao","year":"2021","unstructured":"Gao X, Shi F, Shen D, Liu M. Task-induced pyramid and attention gan for multimodal brain image imputation and classification in alzheimer\u2019s disease. IEEE J Biomed And Health Inf. 2021;26(1):36\u201343.","journal-title":"IEEE J Biomed And Health Inf"},{"issue":"4","key":"1836_CR33","doi-asserted-by":"publisher","first-page":"957","DOI":"10.3390\/math11040957","volume":"11","author":"WN Ismail","year":"2023","unstructured":"Ismail WN, Fathimathul Rajeena PP, Ali MA. A meta-heuristic multi-objective optimization method for alzheimer\u2019s disease detection based on multi-modal data. Mathematics. 2023;11(4):957.","journal-title":"Mathematics"},{"key":"1836_CR34","first-page":"1","volume":"71","author":"L Jiaguang","year":"2022","unstructured":"Jiaguang L, Wei Y, Wang C, Qian H, Liu Y, Long X. 3-d cnn-based multichannel contrastive learning for alzheimer\u2019s disease automatic diagnosis. IEEE Trans Instrum Meas. 2022;71:1\u201311.","journal-title":"IEEE Trans Instrum Meas"},{"issue":"3","key":"1836_CR35","doi-asserted-by":"publisher","first-page":"2743","DOI":"10.1007\/s11760-023-02945-w","volume":"18","author":"F Uyguro\u011flu","year":"2024","unstructured":"Uyguro\u011flu F, Toygar \u00d6, Demirel H. Cnn-based alzheimer\u2019s disease classification using fusion of multiple 3d angular orientations. Signal, Image Video Process. 2024;18(3):2743\u201351.","journal-title":"Signal, Image Video Process"},{"issue":"1","key":"1836_CR36","doi-asserted-by":"publisher","first-page":"27756","DOI":"10.1038\/s41598-024-76313-0","volume":"14","author":"LS Saoud","year":"2024","unstructured":"Saoud LS, AlMarzouqi H. Explainable early detection of alzheimer\u2019s disease using rois and an ensemble of 138 3d vision transformers. Sci Rep. 2024;14(1):27756.","journal-title":"Sci Rep"},{"key":"1836_CR37","doi-asserted-by":"crossref","unstructured":"Kim SK, Duong QA, Gahm JK. Multimodal 3d deep learning for early diagnosis of alzheimer\u2019s disease. IEEE Access. 2024.","DOI":"10.1109\/ACCESS.2024.3381862"},{"issue":"12","key":"1836_CR38","doi-asserted-by":"publisher","first-page":"5648","DOI":"10.3390\/s23125648","volume":"23","author":"EH Alyoubi","year":"2023","unstructured":"Alyoubi EH, Moria KM, Alghamdi JS, Tayeb HO. An optimized deep learning model for predicting mild cognitive impairment using structural mri. Sensors. 2023;23(12):5648.","journal-title":"Sensors"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01836-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-025-01836-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01836-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T10:15:45Z","timestamp":1757326545000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-025-01836-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,31]]},"references-count":38,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1836"],"URL":"https:\/\/doi.org\/10.1186\/s12880-025-01836-5","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,31]]},"assertion":[{"value":"12 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The case data included in this study were all from the public dataset available on the ADNI official website (), and ethical review information regarding ADNI data is available on the website.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"309"}}