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Recently, many sMRI\u2010based deep learning methods have been developed for AD diagnosis. Some of these methods utilize neural networks to extract high\u2010level representations on the basis of handcrafted features, while others attempt to learn useful features from brain regions proposed by a separate module. However, these methods require considerable manual engineering. Their stepwise training procedures would introduce cascading errors. Here, we propose the parallel attention\u2010augmented bilinear network, a novel deep learning framework for AD diagnosis. Based on a 3D convolutional neural network, the framework directly learns both global and local features from sMRI scans without any prior knowledge. The framework is lightweight and suitable for end\u2010to\u2010end training. We evaluate the framework on two public datasets (ADNI\u20101 and ADNI\u20102) containing 1,340 subjects. On both the AD classification and mild cognitive impairment conversion prediction tasks, our framework achieves competitive results. Furthermore, we generate heat maps that highlight discriminative areas for visual interpretation. Experiments demonstrate the effectiveness of the proposed framework when medical priors are unavailable or the computing resources are limited. The proposed framework is general for 3D medical image analysis with both efficiency and interpretability.<\/jats:p>","DOI":"10.1002\/hbm.25685","type":"journal-article","created":{"date-parts":[[2021,10,22]],"date-time":"2021-10-22T07:33:39Z","timestamp":1634888019000},"page":"760-772","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["A parallel attention\u2010augmented bilinear network for early magnetic resonance imaging\u2010based diagnosis of Alzheimer's disease"],"prefix":"10.1002","volume":"43","author":[{"given":"Hao","family":"Guan","sequence":"first","affiliation":[{"name":"School of Computer Science, Faculty of Engineering The University of Sydney  Darlington New South Wales Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaoyue","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Faculty of Engineering The University of Sydney  Darlington New South Wales Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Cheng","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Big Data\u2010Based Precision Medicine Beihang University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Jing","sequence":"additional","affiliation":[{"name":"China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital Capital Medical University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7783-3073","authenticated-orcid":false,"given":"Tao","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Big Data\u2010Based Precision Medicine Beihang University  Beijing China"},{"name":"Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering Beihang University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,10,22]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awr020"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2015.01.048"},{"key":"e_1_2_11_4_1","unstructured":"Chen Y. Kalantidis Y. Li J. Yan S. &Feng J.(2018).A2\u2010nets: Double attention networks.Paper presented at the Proceedings of the 32nd Conference on Neural Information Processing Systems(NeurIPS 2018) Montr\u00e9al Canada. 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Bordes A. &Bengio Y.(2015).Deep sparse rectifier neural networks.Paper presented at the Proceedings of the International Conference on Artificial Intelligence and Statistics. (Vol. 64 pp. 315\u2010323). Amsterdam Netherlands: Elsevier.https:\/\/doi.org\/10.1016\/j.neunet.2014.12.006","DOI":"10.1016\/j.neunet.2014.12.006"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/11866763_8"},{"key":"e_1_2_11_14_1","doi-asserted-by":"crossref","unstructured":"He K. Zhang X. Ren S. &Sun J.(2016a).Deep residual learning for image recognition.Paper presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(pp. 770\u2013778).https:\/\/doi.org\/10.1109\/cvpr.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_11_15_1","doi-asserted-by":"crossref","unstructured":"He K. Zhang X. Ren S. &Sun J.(2016b).Identity mappings in deep residual networks. In Leibe B. Matas J. Sebe N. & Welling M. (Eds.) 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N. &Lopez\u2010Paz D.(2017).Mixup: Beyond empirical risk minimization.arXiv 504\u2010519.https:\/\/doi.org\/10.1007\/978-3-030-01231-1_31","DOI":"10.1007\/978-3-030-01231-1_31"},{"key":"e_1_2_11_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2582386"},{"key":"e_1_2_11_58_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.12.001"},{"key":"e_1_2_11_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2868086"}],"container-title":["Human Brain Mapping"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/hbm.25685","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/hbm.25685","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/hbm.25685","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T11:28:16Z","timestamp":1701257296000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/hbm.25685"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,22]]},"references-count":58,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["10.1002\/hbm.25685"],"URL":"https:\/\/doi.org\/10.1002\/hbm.25685","archive":["Portico"],"relation":{},"ISSN":["1065-9471","1097-0193"],"issn-type":[{"value":"1065-9471","type":"print"},{"value":"1097-0193","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,22]]},"assertion":[{"value":"2021-08-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-09-28","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-10-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}