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The Journal of Supercomputing
Published in: The Journal of Supercomputing 2/2023

22-07-2022

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

Authors: Fujia Ren, Chenhui Yang, Y. A. Nanehkaran

Published in: The Journal of Supercomputing | Issue 2/2023

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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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Literature
1.
Petersen RC, Doody R, Kurz A, Mohs RC, Morris JC, Rabins PV, Ritchie K, Rossor M, Thal L, Winblad B (2001) Current concepts in mild cognitive impairment. Arch Neurol 58(12):1985–1992
2.
de la Torre JC (2010) Alzheimer’s disease is incurable but preventable. J Alzheimer’s dis 20(3):861–870
3.
Tramutola A, Triplett JC, Di Domenico F, Niedowicz DM, Murphy MP, Coccia R, Perluigi M, Butterfield DA (2015) Alteration of mtor signaling occurs early in the progression of Alzheimer disease (ad): analysis of brain from subjects with pre-clinical ad, amnestic mild cognitive impairment and late-stage ad. J neurochem 133(5):739–749
4.
Pellegrini E, Ballerini L, Hernandez MdCV, Chappell FM, González-Castro V, Anblagan D, Danso S, Muñoz-Maniega S, Job D, Pernet C et al (2018) Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: a systematic review. Alzheimer’s Dement Diagn Assess Dis Monit 10:519–535
5.
Jo T, Nho K, Saykin AJ (2019) Deep learning in Alzheimer’s disease: diagnostic classification and prognostic prediction using neuroimaging data. Front Aging Neurosci 11:220
6.
7.
Fu L, Li Z, Ye Q, Yin H, Liu Q, Chen X, Fan X, Yang W, Yang G (2022) Learning robust discriminant subspace based on joint l2, p-and l2, s-norm distance metrics. IEEE Trans Neural Netw and Learn Syst 33(1):130–144MathSciNet
8.
Ye Q, Li Z, Fu L, Zhang Z, Yang W, Yang G (2019) Nonpeaked discriminant analysis for data representation. IEEE Trans Neural Netw Learn Syst 30(12):3818–3832MathSciNet
9.
Feng Q, Ding Z (2020) MRI radiomics classification and prediction in Alzheimer’s disease and mild cognitive impairment: a review. Curr Alzheimer Res 17(3):297–309
10.
Marcus C, Mena E, Subramaniam RM (2014) Brain pet in the diagnosis of Alzheimer’s disease. Clin Nuclear Med 39(10):413
11.
Petersen RC (2010) Alzheimer’s disease: progress in prediction. The Lancet Neurol 9(1):4–5
12.
Dubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K, DeKosky ST, Gauthier S, Selkoe D, Bateman R et al (2014) Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. The Lancet Neurol 13(6):614–629
13.
Risacher SL, Saykin AJ, Wes JD, Shen L, Firpi HA, McDonald BC (2009) Baseline MRI predictors of conversion from MCI to probable ad in the ADNI cohort. Curr Alzheimer Res 6(4):347–361
14.
Zhu Y, Zhuang F, Wang J, Ke G, Chen J, Bian J, Xiong H, He Q (2020) Deep subdomain adaptation network for image classification. IEEE Trans Neural Netw Learn Syst 32(4):1713–1722MathSciNet
15.
Sarker IH (2021) Machine learning: algorithms, real-world applications and research directions. SN Comput Sci 2(3):1–21MathSciNet
16.
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770–778. IEEE
17.
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4700–4708. IEEE
18.
Dong S, Wang P, Abbas K (2021) A survey on deep learning and its applications. Comput Sci Rev 40:110379MathSciNet
19.
20.
Huang G, Chen X, Zhang Y, Zhang X (2012) Towards architecture-based management of platforms in the cloud. Front Comput Sci 6(4):388–397MathSciNet
21.
Chen X, Zhang J, Lin B, Chen Z, Wolter K, Min G (2021) Energy-efficient offloading for DNN-based smart ioT systems in cloud-edge environments. IEEE Trans Parallel Distrib Syst 33(3):683–697
22.
Zhang T, Liao Q, Zhang D, Zhang C, Yan J, Ngetich R, Zhang J, Jin Z, Li L (2021) Predicting MCI to ad conversation using integrated SMRI and RS-FMRI: machine learning and graph theory approach. Front Aging Neurosci 13:429
23.
Kruthika K, Maheshappa H, Initiative ADN et al (2019) Multistage classifier-based approach for Alzheimer’s disease prediction and retrieval. Inform Med Unlocked 14:34–42
24.
SøRensen L, Igel C, Liv Hansen N, Osler M, Lauritzen M, Rostrup E, Nielsen M (2016) Initiative ADN, the Australian imaging biomarkers, of ageing, LFS: early detection of Alzheimer’s disease using MRI hippocampal texture. Hum Brain Mapp 37(3):1148–1161
25.
Popuri K, Ma D, Wang L, Beg MF (2020) Using machine learning to quantify structural MRI neurodegeneration patterns of Alzheimer’s disease into dementia score: Independent validation on 8,834 images from ADNI, AIBl, OASIS, and MIRIAD databases. Hum Brain Mapp 41(14):4127–4147
26.
Dai N, Kang H, Jones GL, Fiecas MB, Initiative ADN (2021) A bayesian latent spatial model for mapping the cortical signature of progression to Alzheimer’s disease. Can J Stat 49(1):46–62MathSciNet
27.
Farooq A, Anwar S, Awais M, Rehman S (2017) A deep CNN based multi-class classification of alzheimer’s disease using MRI. In: 2017 IEEE International Conference on Imaging Systems and Techniques (IST), pp. 1–6. IEEE
28.
Lin W, Tong T, Gao Q, Guo D, Du X, Yang Y, Guo G, Xiao M, Du M, Qu X et al (2018) Convolutional neural networks-based MRI image analysis for the Alzheimer’s disease prediction from mild cognitive impairment. Front Neurosci 12:777
29.
Shen T, Jiang J, Li Y, Wu P, Zuo C, Yan Z (2018) Decision supporting model for one-year conversion probability from mci to ad using CNN and SVM. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 738–741. IEEE
30.
Feng W, Halm-Lutterodt NV, Tang H, Mecum A, Mesregah MK, Ma Y, Li H, Zhang F, Wu Z, Yao E et al (2020) Automated MRI-based deep learning model for detection of Alzheimer’s disease process. Int J Neural Syst 30(06):2050032
31.
Pan X, Phan T-L, Adel M, Fossati C, Gaidon T, Wojak J, Guedj E (2020) Multi-view separable pyramid network for ad prediction at MCI stage by 18 f-FDG brain pet imaging. IEEE Trans Med Imaging 40(1):81–92
32.
Wegmayr V, Hörold M, Buhmann JM (2019) Generative aging of brain MRI for early prediction of MCI-AD conversion. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019). 1042–1046. IEE
33.
Basheera S, Ram MSS (2021) Deep learning based Alzheimer’s disease early diagnosis using T2w segmented gray matter MRI. Intl J Imaging Syst Technol 31(3):1692–1710
34.
Li H, Habes M, Wolk DA, Fan Y, Initiative ADN et al (2019) A deep learning model for early prediction of Alzheimer’s disease dementia based on hippocampal magnetic resonance imaging data. Alzheimer’s Dement 15(8):1059–1070
35.
Valverde JM, Imani V, Abdollahzadeh A, De Feo R, Prakash M, Ciszek R, Tohka J (2021) Transfer learning in magnetic resonance brain imaging: a systematic review. J Imaging 7(4):66
36.
Cheng B, Liu M, Suk H-I, Shen D, Zhang D (2015) Multimodal manifold-regularized transfer learning for mci conversion prediction. Brain Imaging Behav 9(4):913–926
37.
Wachinger C, Reuter M, Initiative ADN et al (2016) Domain adaptation for Alzheimer’s disease diagnostics. Neuroimage 139:470–479
38.
Cheng B, Liu M, Zhang D, Munsell BC, Shen D (2015) Domain transfer learning for MCI conversion prediction. IEEE Trans Biomed Eng 62(7):1805–1817
39.
Cheng B, Zhang D, Shen D (2012) Domain transfer learning for mci conversion prediction. In: International Conference on Medical Image Computing and Computer-Assisted Intervention pp. 82–90. Springer
40.
Cheng B, Liu M, Shen D, Li Z, Zhang D (2017) Multi-domain transfer learning for early diagnosis of Alzheimer’s disease. Neuroinformatics 15(2):115–132
41.
Prakash D, Madusanka N, Bhattacharjee S, Kim C-H, Park H-G, Choi H-K (2021) Diagnosing alzheimer’s disease based on multiclass mri scans using transfer learning techniques. Current medical imaging 17(12):1460–1472
42.
Aderghal K, Afdel K, Benois-Pineau J, Catheline G, Initiative ADN et al (2020) Improving alzheimer’s stage categorization with convolutional neural network using transfer learning and different magnetic resonance imaging modalities. Heliyon 6(12):05652
43.
Ocasio E, Duong TQ (2021) Deep learning prediction of mild cognitive impairment conversion to alzheimer’s disease at 3 years after diagnosis using longitudinal and whole-brain 3d mri. PeerJ Computer Science 7:560
44.
Mehmood A, Yang S, Feng Z, Wang M, Ahmad AS, Khan R, Maqsood M, Yaqub M (2021) A transfer learning approach for early diagnosis of Alzheimer’s disease on MRI images. Neuroscience 460:43–52
45.
Zhou S, Cox CR, Lu H (2019) Improving whole-brain neural decoding of FMRI with domain adaptation. In: International workshop on machine learning in medical imaging, pp. 265–273. Springer
46.
Naz S, Ashraf A, Zaib A (2022) Transfer learning using freeze features for Alzheimer neurological disorder detection using ADNI dataset. Multimed Syst 28(1):85–94
47.
Bae J, Stocks J, Heywood A, Jung Y, Jenkins L, Hill V, Katsaggelos A, Popuri K, Rosen H, Beg MF et al (2021) Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer’s type based on a three-dimensional convolutional neural network. Neurobiol Aging 99:53–64
48.
Abrol A, Bhattarai M, Fedorov A, Du Y, Plis S, Calhoun V, Initiative ADN et al (2020) Deep residual learning for neuroimaging: an application to predict progression to Alzheimer’s disease. J Neurosci Methods 339:108701
49.
Oh K, Chung Y-C, Kim KW, Kim W-S, Oh I-S (2019) Classification and visualization of Alzheimer’s disease using volumetric convolutional neural network and transfer learning. Sci Rep 9(1):1–16
50.
Aderghal K, Khvostikov A, Krylov A, Benois-Pineau J, Afdel K, Catheline G (2018) Classification of Alzheimer disease on imaging modalities with deep CNNS using cross-modal transfer learning. In: 2018 IEEE 31st international symposium on computer-based medical systems (CBMS), pp. 345–350. IEEE
51.
Gamberger D, Lavrač N, Srivatsa S, Tanzi RE, Doraiswamy PM (2017) Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer’s disease. Sci Rep 7(1):1–12
52.
Nezhadmoghadam F, Martinez-Torteya A, Treviño V, Martínez E, Santos A, Tamez-Peña J, Initiative ADN et al (2021) Robust discovery of mild cognitive impairment subtypes and their risk of Alzheimer’s disease conversion using unsupervised machine learning and gaussian mixture modeling. Curr Alzheimer Res 18(7):595–606
53.
Guo M, Li Y, Zheng W, Huang K, Zhou L, Hu X, Yao Z, Hu B (2020) A novel conversion prediction method of MCI to ad based on longitudinal dynamic morphological features using ADNI structural MRIS. J Neurol 267(10):2983–2997
54.
Varatharajah Y, Ramanan VK, Iyer R, Vemuri P (2019) Predicting short-term MCI-to-AD progression using imaging, CSF, genetic factors, cognitive resilience, and demographics. Sci Rep 9(1):1–15
55.
Pan D, Zeng A, Jia L, Huang Y, Frizzell T, Song X (2020) Early detection of Alzheimer’s disease using magnetic resonance imaging: a novel approach combining convolutional neural networks and ensemble learning. Front Neurosci 14:259
56.
Nanni L, Interlenghi M, Brahnam S, Salvatore C, Papa S, Nemni R, Castiglioni I, Initiative ADN, et al. (2020) Comparison of transfer learning and conventional machine learning applied to structural brain MRI for the early diagnosis and prognosis of Alzheimer’s disease. Front Neurol 345
57.
Chen X, Li M, Zhong H, Ma Y, Hsu C-H (2021) Dnnoff: offloading DNN-based intelligent ioT applications in mobile edge computing. IEEE transactions on industrial informatics.18(4):2820-9
58.
Chen X, Hu J, Chen Z, Lin B, Xiong N, Min G (2021) A reinforcement learning-empowered feedback control system for industrial internet of things. IEEE Trans Ind Inform 18(4):2724–2733
59.
Huang G, Luo C, Wu K, Ma Y, Zhang Y, Liu X (2019) Software-defined infrastructure for decentralized data lifecycle governance: principled design and open challenges. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 1674–1683. IEEE
60.
Kaur PD, Sharma P (2020) Ic-smart: iotcloud enabled seamless monitoring for Alzheimer diagnosis and rehabilitation system. J Ambient Intell Humaniz Comput 11(8):3387–3403
61.
Varatharajan R, Manogaran G, Priyan MK, Sundarasekar R (2018) Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Clust Comput 21(1):681–690
62.
Yadav P, Kumar P, Kishan P, Raj P, et al. (2021) Development of pervasive iot based healthcare monitoring system for alzheimer patients. In: Journal of physics: Conference Series, vol. 2007, p. 012035. IOP Publishing
63.
Sharma S, Dudeja RK, Aujla GS, Bali RS, Kumar N (2020) Detras: deep learning-based healthcare framework for ioT-based assistance of Alzheimer patients. Neural Comput Appl 17:1–3
64.
65.
Vapnik V (1992) Principles of risk minimization for learning theory. In: Advances in neural information processing systems, pp. 831–838. Springer
66.
Woo S, Park J, Lee J-Y, So Kweon I (2018) CBAM: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19. Springer
67.
Long M, Cao Y, Wang J, Jordan M (2015) Learning transferable features with deep adaptation networks. In: International Conference on Machine Learning, pp. 97–105. PMLR
68.
Davatzikos C, Bhatt P, Shaw LM, Batmanghelich KN, Trojanowski JQ (2011) Prediction of MCI to ad conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiol Aging 32(12):2322.e19
69.
Gaser C, Kurth F (2017) Manual computational anatomy toolbox-cat12. University of Jena, Structural brain mapping Group at the Departments of Psychiatry and Neurology
Metadata
Title
MRI-based model for MCI conversion using deep zero-shot transfer learning
Authors
Fujia Ren
Chenhui Yang
Y. A. Nanehkaran
Publication date
22-07-2022
Publisher
Springer US
Published in
The Journal of Supercomputing / Issue 2/2023
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-022-04668-0

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