A Robust Deep Model for Improved Classification of AD/MCI Patients

IEEE Journal of Biomedical and Health Informatics

Volumn 19, Issue 5, 2015, Pages 1610-1616

  Li, Feng ^a   Tran, Loc ^a   Thung, Kim Han ^b   Ji, Shuiwang ^c   Shen, Dinggang ^b,d   Li, Jiang ^a  

^a Old Dominion University   (United States)

^b UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^c Old Dominion University   (United States)

^d Korea University   (South Korea)

Author keywords

Alzheimer's Disease; Deep Learning; Early Diagnosis; MRI; PET

Indexed keywords

COMPUTER AIDED DIAGNOSIS; LEARNING SYSTEMS; MAGNETIC RESONANCE IMAGING; NEURODEGENERATIVE DISEASES; POLYETHYLENE TEREPHTHALATES;

ADAPTIVE LEARNING; ALZHEIMER'S DISEASE; CLASSIFICATION ACCURACY; EARLY DIAGNOSIS; LEARNING FRAMEWORKS; MILD COGNITIVE IMPAIRMENTS (MCI); NON-INVASIVE IMAGING; STABILITY SELECTIONS;

DEEP LEARNING;

ALZHEIMER DISEASE; COGNITIVE DYSFUNCTION; COMPUTER ASSISTED DIAGNOSIS; EARLY DIAGNOSIS; HUMAN; MACHINE LEARNING; NUCLEAR MAGNETIC RESONANCE IMAGING; POSITRON EMISSION TOMOGRAPHY; PRINCIPAL COMPONENT ANALYSIS; PROCEDURES; SUPPORT VECTOR MACHINE; THEORETICAL MODEL;

ALZHEIMER DISEASE; COGNITIVE DYSFUNCTION; EARLY DIAGNOSIS; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MACHINE LEARNING; MAGNETIC RESONANCE IMAGING; MODELS, THEORETICAL; POSITRON-EMISSION TOMOGRAPHY; PRINCIPAL COMPONENT ANALYSIS; SUPPORT VECTOR MACHINE;

EID: 84940975497     PISSN: 21682194     EISSN: 21682208     Source Type: Journal    
DOI: 10.1109/JBHI.2015.2429556     Document Type: Article

References (29)

1. Alzheimer's Association: 2012, "Alzheimer's disease facts and figures," Alzheimer's Dementia, vol. 8, no. 2, pp. 131-168, 2012
2. C. Davatzikos, P. Bhatt, L. M. Shaw, K. N. Batmanghelich, and J. Q. Trojanowski, "Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification," Neurobiol. Aging, vol. 32, no. 12, pp. 2322. e19-2322. e27, 2011
3. A. Nordberg, J. O. Rinne, A. Kadir, and B. Langstrom, "The use of PET in Alzheimer disease," Nature Rev. Neurol., vol. 6, no. 2, pp. 78-87, 2010
4. M. D. Greicius, G. Srivastava, A. L. Reiss, and V. Menon, "Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI," in Proc. Nat. Acad. Sci. United States Amer., vol. 101, no. 13, pp. 4637-4642, 2004
5. H. Suk and D. Shen, "Deep learning-based feature representation for AD/MCI classification," in Proc. Med. Image Comput. Comput.-Assisted Intervention Conf., 2013, pp. 583-590
6. G. E. Hinton, S. G. Osindero, and Y. W. Teh, "A fast learning algorithm for deep belief nets," Neural Comput., vol. 18, no. 7, pp. 1527-1554, 2006
7. 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
8. J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Ng, "Multimodal deep learning," in Proc. Int. Conf. Mach. Learn., 2011, pp. 689-696
9. G. E. Hinton, N. Srivastave, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, "Improving neural networks by preventing coadaptation of feature detectors," arXiv:1207. 0580, 2012
10. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "A simple way to prevent neural networks from overfitting," J. Mach. Learn. Res., vol. 15, pp. 1929-1958, 2014
11. N. Meinshausen and P. Buhlmann, "Stability selection," J. Roy. Statist. Soc. B, vol. 72, pp. 417-473, 2010
12. R. Tibshirani, "Regression shrinkage and selection via the Lasso," J. Roy. Statist. Soc., Ser. B, vol. 58, no. 1, pp. 267-288, 1996
13. V. C. Pangman, J. Sloan, and L. Guse, "An examination of psychometric properties of the mini-Mental state examination and the standardized mini-Mental state examination: Implications for clinical practice," Appl. Nursing Res., vol. 13, no. 4, pp. 209-213, 2000
14. E. Kolibas, V. Korinkova, V. Novotny, K. Vajdickova, and D. Hunakova, "ADAS-cog (Alzheimer's disease assessment scale-cognitive subscale)-validation of the Slovak version," Bratisl Lek Listy, vol. 101, no. 11, pp. 598-602, 2000
15. F. Li, L. Tran, KH. Thung, S. Ji, D. Shen, and J. Li, "Robust deep learning for improved classification of AD/MCI patients," Mach. Learn. Med. Imag., vol. 8679, pp. 240-247, 2014
16. N. Kabani, D. MacDonald, C. Holmes, and A. Evans, "A 3D atlas of the human brain," NeuroImage, vol. 7, no. 4, p. S717, 1998
17. C. Hinrichs, V. Singh, G. Xu, and S. C. Johnson," 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
18. I. T. Jolliffe, Principal Component Analysis (Springer Series in Statistics), 2nd ed. New York, NY, USA: Springer, 2002
19. D. Erhan, Y. Bengio, A. Courville, P. A. Manzagol, P. Vincent, and S. Bengio, "Why does unsupervised pre-training help deep learning" J. Mach. Learn. Res., vol. 11, pp. 625-660, 2010
20. Y. Bengio, "Learning deep architectures for AI," Found. Trends Mach. Learning, vol. 2, no. 1, p. 1-127, 2009
21. K. Cho, A. Ilin, and T. Raiko, "Improved learning of Gaussian-Bernoulli restricted Boltzmannmachines," in Proc. Artif. NeuralNetw. Mach. Learn. Conf., 2011, pp. 10-17
22. G. Hinton, S. Osindero, and Y.-W. Teh, "A fast learning algorithm for deep belief nets," Neural Comput., vol. 18, no. 1527-1554, 2006
23. B. Heisele, T. Serre, M. Pontil, T. Vetter, and T. Poggio, "Categorization by learning and combining object parts," in Proc. Neural Inf. Process. Syst., 2001, pp. 1239-1245
24. S. Ji and J. Ye, "An accelerated gradient method for trace norm minimization," in Proc. 26th Ann. Int. Conf. Mach. Learn., 2009, pp. 457-464
25. Y. Xue, X. Liao, L. Carin, and B. Krishnapuram, "Multi-task learning for classification with Dirichlet process priors," J. Mach. Learn. Res., vol. 8, pp. 35-63, 2007
26. C. Cortes and V. Vapnik, "Support-vector networks," Mach. Learning, vol. 20, no. 3, pp. 273-297, 1995
27. A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli,. M. Saisana, and S. Tarantola, Global Sensitivity Analysis. The Primer. New York, NY, USA: Wiley, 2008
28. D. Zhang, Y. Wang, L. Zhou, H. Yuan, and D. Shen, "Multimodal classification of Alzheimers disease and mild cognitive impairment," NeuroImage, vol. 55, no. 3, pp. 856-867, 2011
29. R. Caruana, "Multitask learning: A knowledge-based source of inductive bias," Mach. Learn., vol. 28, pp. 41-75, 1997.