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MRI-based model for MCI conversion using deep zero-shot transfer learning

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Abstract

This study describes a deep zero-shot transfer learning model (DZTLM) for predicting mild cognitive impairment (MCI) in patients with Alzheimer’s disease (AD). The proposed DZTLM combines ResNet and deep subdomain adaptation network (DsAN) blocks with a simple data augmentation and transfer technique, Elastic-Mixup. We test the DZTLM using 3D gray matter images segregated from structural MRI as input. Ablation experiments are conducted to evaluate the proposed model and compare it with existing approaches. Experiments demonstrate that the DsAN network coordinating Elastic-Mixup enhances the accuracy of MCI-AD prediction by more than 18% compared with a standard 3D ResNet50 classifier. The Elastic-Mixup technique contributes more than 16% to this increase in prediction accuracy. Elastic-Mixup also enhances the sensitivity of recognition for stable MCI. When labeled samples are scarce, the unsupervised DZTLM outperforms a semi-supervised transfer learning model. The DZTLM achieves comparable outcomes to existing models despite the absence of tagged MRI data.

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Data availability

This article is supported by data that can be found in ADNI, which can be accessed from http://adni.loni.usc.edu/.

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Acknowledgements

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12- f2–0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. HoffmannLa Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson&Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. The ADNI data are disseminated by the Laboratory for Neuro-Imaging at the University of Southern California. This work was supported by the National Key Research Development Program of China (Grant No. 2020YFB1313703), the National Natural Science Foundation of China (Grant No.6002304), and the Natural Science Foundation of Fujian Province of China (Grant No. 2020J05002).

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Authors and Affiliations

  1. School of Informatics, Xiamen University, 422 Siming Road South, Xiamen, 361005, Fujian, China

    Fujia Ren & Chenhui Yang

  2. School of Big Data and Computer Science, Guizhou Normal University, 270 Waihuandonglu Guiyang, Guiyang, 50001, Guizhou, China

    Fujia Ren

  3. College of Information Engineering, Yancheng Teachers University, 50 Kaifang Avenue, Yancheng, 224002, Jiangsu, China

    Y. A. Nanehkaran

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Correspondence to Chenhui Yang.

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Ren, F., Yang, C. & Nanehkaran, Y.A. MRI-based model for MCI conversion using deep zero-shot transfer learning. J Supercomput 79, 1182–1200 (2023). https://doi.org/10.1007/s11227-022-04668-0

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