Multimodal Neuroimaging Feature Learning with Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease

IEEE Journal of Biomedical and Health Informatics

Volumn 22, Issue 1, 2018, Pages 173-183

  Shi, Jun ^a   Zheng, Xiao ^a   Li, Yan ^b   Zhang, Qi ^a   Ying, Shihui ^a  

^a SHANGHAI UNIVERSITY   (China)

^b SHENZHEN UNIVERSITY   (China)

Author keywords

Alzheimer's disease; deep learning; deep polynomial networks; multimodal neuroimaging; multimodal stacked deep polynomial networks

Indexed keywords

CLASSIFICATION (OF INFORMATION); DEEP LEARNING; DIAGNOSIS; LARGE DATASET; MAGNETIC RESONANCE IMAGING; NEURODEGENERATIVE DISEASES; NEUROIMAGING; POLYNOMIALS; POSITRON EMISSION TOMOGRAPHY;

ALZHEIMER'S DISEASE; BINARY CLASSIFICATION; FEATURE REPRESENTATION; MILD COGNITIVE IMPAIRMENTS; MULTI-CLASS CLASSIFICATION; MULTI-MODAL; POLYNOMIAL NETWORKS; POSITRON EMISSION TOMOGRAPHY (PET);

LEARNING ALGORITHMS;

ALZHEIMER DISEASE; ARTICLE; CEREBROSPINAL FLUID; CLASSIFICATION; COGNITIVE DEFECT; DEEP POLYNOMIAL NETWORK ALGORITHM; GRAY MATTER; LEARNING ALGORITHM; MULTIMODAL IMAGING; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; POSITRON EMISSION TOMOGRAPHY; RECEIVER OPERATING CHARACTERISTIC; SUPPORT VECTOR MACHINE; ALGORITHM; ARTIFICIAL NEURAL NETWORK; DIAGNOSTIC IMAGING; HUMAN; MACHINE LEARNING; PROCEDURES;

ALGORITHMS; ALZHEIMER DISEASE; HUMANS; MACHINE LEARNING; MULTIMODAL IMAGING; NEURAL NETWORKS (COMPUTER); NEUROIMAGING; ROC CURVE;

EID: 85040340661     PISSN: 21682194     EISSN: 21682208     Source Type: Journal    
DOI: 10.1109/JBHI.2017.2655720     Document Type: Article

References (55)

1. G. McKhann, D. Drachman, M. Folstein, R. Katzman, D. Price, and E. M. Stadlan, "Clinical diagnosis of Alzheimer's disease report of the NINCDSADRDA work group under the auspices of department of health and human services task force on Alzheimer's disease, " Neurology, vol. 34, no. 7, pp. 939-939, 1984.
2. R. Brookmeyer, E. Johnson, K. Ziegler-Graham, and H. Michael Arrighi, "Forecasting the global burden of Alzheimer's disease, " Alzheimers Dementia, vol. 3, no. 3, pp. 186-191, 2007.
3. J. A. Turner, S. G. Potkin, G. G. Brown, D. B. Keator, G. McCarthy, and G. H. Glover, "Neuroimaging for the diagnosis and study of psychiatric disorders, " IEEE Signal Process. Mag., vol. 24, no. 4, pp. 112-117, Jul. 2007.
4. S. D. Liu et al., "Multimodal neuroimaging computing: A review of the applications in neuropsychiatric disorders, " Brain Informat., vol. 2, pp. 167-180, 2015.
5. L. Alves, A. S. A. Correia, R. Miguel, P. Alegria, and P. Bugalho, "Alzheimer's disease: A clinical practice-oriented review, " Front. Neurol., vol. 3, pp. 1-20, 2012.
6. S. L. Risacher and A. J. Saykin, "Neuroimaging and other biomarkers for Alzheimer's disease: The changing landscape of early detection, " Annu. Rev. Clin. Psychol., vol. 9, pp. 621-648, 2013.
7. R. M. Ahmedet al., "Biomarkers in dementia: Clinical utility and new directions, " J. Neurol. Neurosurg. Psych., vol. 85, pp. 1426-1434, 2014.
8. R. Cuingnetet al., "Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database, " NeuroImage, vol. 56, no. 2, pp. 766-781, 2011.
9. F. Falahati, E. Westman, and A. Simmons, "Multivariate data analysis and machine learning in Alzheimer's disease with a focus on structural magnetic resonance imaging, " J. Alzheimers Dis., vol. 41, no. 3, pp. 685-708, 2014.
10. B. Mwangi, T. S. Tian, and J. C. Soares, "A review of feature reduction techniques in neuroimaging, " Neuroinformatics, vol. 12, pp. 229-244, 2014.
11. K. Uludag, and A. Roebroeck, "General overview on the merits of multimodal neuroimaging data fusion, " NeuroImage, vol. 102, pp. 3-10, 2014.
12. B. K. Cao, X. N. Kong, and P. S. Yu, "A review of heterogeneous data mining for brain disorder identification, " Brain Informat., vol. 2, no. 4, pp. 253-264, 2015.
13. C. Hinrichs, V. Singh, G. F. Xu, S. C. Johnson, and ADNI, "Predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population, " NeuroImage, vol. 55, no. 2, pp. 574-589, 2011.
14. D. Q. Zhang, Y. P. Wang, L. P. Zhou, H. Yuan, D. G. Shen, and ADNI, "Multimodal classification of Alzheimer's disease and mild cognitive impairment, " NeuroImage, vol. 55, pp. 856-867, 2011.
15. K. R. Gray, P. Aljabar, R. A. Heckemann, A. Hammers, D. Rueckert, and ADNI, "Random forest-based similarity measures for multi-modal classification of Alzheimer's disease, " NeuroImage, vol. 65, pp. 167-175, 2013.
16. F. Y. Liu, L. P. Zhou, C. H. Shen, and J. P. Yin, "Multiple kernel learning in the primal for multi-modal Alzheimer's disease classification, " IEEE J. Biomed. Health Informat., vol. 18, no. 3, pp. 984-990, May 2014.
17. M. Dyrba, M. Grothe, T. Kirste, and S. J. Teipel, "Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM, " Hum. Brain Mapping, vol. 36, no. 6, pp. 2118-2131, 2015.
18. B. Jie, D. Q. Zhang, B. Cheng, D. G. Shen, and ADNI, "Manifold regularized multitask feature learning for multimodality disease classification, " Hum. Brain Mapping, vol. 36, pp. 489-507, 2015.
19. Y. Bengio, A. Courville, and P. Vincent, "Representation learning: A review and new perspectives, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1798-1828, Aug. 2013.
20. H. I. Suk, S. W. Lee, D. G. Shen, and ADNI, "Hierarchical feature representation and multimodal fusion with deep learning for AD MCI diagnosis, " NeuroImage, vol. 101, pp. 569-582, Nov. 2014.
21. M. H. Liu, D. Q. Zhang, and D. G. Shen, "Ensemble sparse classification of Alzheimer's disease, " NeuroImage, vol. 60, pp. 1106-1116, 2012.
22. P. Coupé et al., "Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease, " NeuroImage, Clin., vol. 1, pp. 141-152, 2012.
23. A. Gupta, M. Ayhan, and A. Maida, "Natural image bases to represent neuroimaging data, " in Proc. Int. Conf. Mach. Learn., 2013, pp. 987-994.
24. A. Payan and G. Montana, "Predicting Alzheimer's disease: A neuroimaging study with 3D convolutional neural networks, " in Proc. Int. Conf. Pattern Recog. Appl. Methods, 2015.
25. G. E. Hinton, S. Osindero, and Y. Teh, "A fast learning algorithm for deep belief nets, " Neural Comput., vol. 18, no. 7, pp. 1527-1554, 2006.
26. G. E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks, " Science, vol. 313, no. 5786, pp. 504-507, 2006.
27. D. Yu and L. Deng, "Deep learning and its applications to signal and information processing, " IEEE Signal Process. Mag., vol. 28, no. 1, pp. 145-154, Jan. 2011.
28. J. Schmidhuber, "Deep learning in neural networks: An overview, " Neural Netw., vol. 61, pp. 85-117, 2015.
29. G. Carneiro, J. C. Nascimento, and A. Freitas, "The segmentation of the left ventricle of the heart from ultrasound data using deep learning architectures and derivative-based search methods, " IEEE Trans. Image Process., vol. 12, no. 3, pp. 968-982, Mar. 2012.
30. D. C. Cires, L. M. Gambardella, A. Giusti, and J. Schmidhuber, "Deep neural networks segment neuronal membranes in electron microscopy images, " in Proc. Neural Inf. Process. Syst. Conf., 2012, pp. 2852-2860.
31. H. C. Shin, M. R. Orton, D. J. Collins, S. J. Doran, and M. O. Leach, "Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4Dpatient data, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1930-1943, Aug. 2013.
32. G. Carneiro and J. C. Nascimento, "Combining multiple dynamic models and deep learning architectures for tracking the left ventricle endocardium in ultrasound data, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 11, pp. 2592-2607, Nov. 2013.
33. H. Roth et al., "A new 2. 5D representation for lymph node detection using random sets of deep convolutional, " in Proc. Int. Conf. Med. Image Comput. Comput. Assist. Interv., 2014, pp. 520-527.
34. M. P. Sergey et al., "Deep learning for neuroimaging: A validation study, " Front. Neurosci., vol. 8, pp. 1-11, 2014.
35. W. L. Zhang et al., "Deep convolutional neural networks for multimodality isointense infant brain image segmentation, " NeuroImage, vol. 108, pp. 214-224, 2015.
36. T. Brosch, R. Tam, and ADNI, "Manifold learning of brain MRIs by deep learning, " in Proc. Int. Conf. Med. Image Comput. Comput. Assist. Interv., 2013, pp. 633-640,.
37. V. K. Ithapu, V. Singh, O. Okonkwo, and S. C. Johnson, "Randomized denoising autoencoders for smaller and efficient imaging based AD clinical trials, " in Proc. Int. Conf. Med. Image Comput. Comput. Assist. Interv., 2014, pp. 470-478,.
38. S. Q. Liu et al., "Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease, " IEEE Trans. Biomed. Eng., vol. 62, no. 4, pp. 1132-1140, Apr. 2015.
39. H. Suk, and D. Shen, "Deep learning-based feature representation for AD/MCI classification, " in Proc. Int. Conf. Med. Image Comput. Comput. Assist. Interv., 2013, pp. 583-590.
40. H. I. Suk, S. W. Lee, D. Shen, and ADNI, "Deep sparse multi-task learning for feature selection in Alzheimer's disease diagnosis, " Brain Struct. Funct., vol. 221, pp. 1-19, 2015.
41. F. Li, L. Tran, K. H. Thung, S. W. Ji, D. G. Shen, and J. Li, "A robust deep model for improved classification of AD/MCI patients, " IEEE J. Biomed. Health Informat., vol. 19, no. 5, pp. 1610-1616, Sep. 2015.
42. B. Shi, Y. Chen, P. Zhang, C. D. Smith, and J. Liu, "Nonlinear feature transformation and deep fusion for Alzheimer's disease staging analysis, " Pattern Recog., vol. 63, pp. 487-498, 2017.
43. R. Livni, S. Shalev-Shwartz, and O. Shamir, "An algorithm for training polynomial networks, " to be published.
44. Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, "Greedy layerwise training of deep networks, " in Proc. Neural Inf. Process. Syst. Conf., 2006, pp. 153-160.
45. C. R. Jack et al., "The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods, " J. Magn. Reson. Imag., vol. 27, pp. 685-691, 2008.
46. J. G. Sled, A. P. Zijdenbos, and A. C. Evans, "A nonparametric method for automatic correction of intensity nonuniformity in MRI data, " IEEE Trans. Med. Imag., vol. 17, no. 1, pp. 87-97, Feb. 1998.
47. Y. P. Wang, J. X. Nie, P. T. Yap, F. Shi, L. Guo, and D. G. Shen, "Robust deformable-surface-based skull-stripping for large-scale studies, " in Proc. Int. Conf. Med. Image Comput. Comput. Assist. Interv., 2011, pp. 635-642.
48. Y. P. Wang et al., "Knowledge-guided robust MRI brain extraction for diverse large-scale neuroimaging studies on humans and non-human primates, " Plos One, vol. 9, p. e77810, 2014.
49. Y. Zhang, M. Brady, and S. Smith, "Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm, " IEEE Trans. Med. Imag., vol. 20, no. 1, pp. 45-57, Jan. 2001.
50. D. G. Shen and C. Davatzikos, "HAMMER: Hierarchical attribute matching mechanism for elastic registration, " IEEE Trans. Med. Imag., vol. 21, no. 11, pp. 1421-1439, Nov. 2002.
51. N. Kabani, D. MacDonald, C. J. Holmes, and A. Evans, "A 3D atlas of the human brain, " NeuroImage, vol. 7, p. S717, 1998.
52. T. Tong, K. Gray, Q Gao, L. Chen, D. Rueckert, and ADNI, "Multimodal classification of Alzheimer's disease using nonlinear graph fusion, " Pattern Recog., vol. 63, pp. 171-181, 2017.
53. V. N. Vapnik, Statistical Learning Theory. New York, NY, USA: Wiley, 1998.
54. C. C. Chang and C. J. Lin, "LIBSVM: A library for support vector machines, " ACM Trans. Intell. Syst. Technol., vol. 2, pp. 1-39, 2001.
55. C. R. Jack et al., "Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade, " Lancet Neurol., vol. 9, pp. 119-128, 2010.