Abstract
Accurate classification of Alzheimer’s Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), plays a critical role in preventing progression of memory impairment and improving quality of life for AD patients. Among many research tasks, it is of particular interest to identify noninvasive imaging biomarkers for AD diagnosis. In this paper, we present a robust deep learning system to identify different progression stages of AD patients based on MRI and PET scans. We utilized the dropout technique to improve classical deep learning by preventing its weight co-adaptation, which is a typical cause of over-fitting in deep learning. In addition, we incorporated stability selection, an adaptive learning factor and a multi-task learning strategy into the deep learning framework. We applied the proposed method to the ADNI data set and conducted experiments for AD and MCI conversion diagnosis. Experimental results showed that the dropout technique is very effective in AD diagnosis, improving the classification accuracies by 6.2% on average as compared to classical deep learning methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Alzheimer’s Association: 2012 Alzheimer’s disease facts and figures. Alzheimer’s & Dementia 8(2), 131–168 (2012)
Davatzikos, C., Bhatt, P., Shaw, L.M., Batmanghelich, K.N., Trojanowski, J.Q.: Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiology of Aging 27, 2322.e19–2322.e27 (2011)
Nordberg, A., Rinne, J.O., Kadir, A., Langstrom, B.: The use of PET in Alzheimer disease. Nature Reviews Neurology 6(2), 78–87 (2010)
Greicius, M.D., Srivastava, G., Reiss, A.L., Menon, V.: Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: Evidence from functional MRI. Proceedings of the National Academy of Sciences of the United States of America 101(13), 4637–4642 (2004)
Suk, H.-I., Shen, D.: Deep learning-based feature representation for AD/MCI classification. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 583–590. Springer, Heidelberg (2013)
Hinton, G.E., Grivastava, Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Computation 18(7), 1527–1554 (2006)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: A review and new perspectives. PAMI 35(8), 1798–1828 (2013)
Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.: Multimodal deep learning. In: ICML, pp. 689–696 (2011)
Hinton, G.E., Srivastave, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580 (2012)
Meinshausen, N., Buhlmann, P.: Stability selection. J. R. Statist. Soc. B, 417–473 (2010)
Tibshirani, R.: Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society, Series B 58(1), 267–288 (1996)
Kabani, N., MacDonald, D., Holmes, C., Evans, A.: A 3D atlas of the human brain. NeuroImage 7(4), S717 (1998)
Hinrichs, C., Singh, V., Xu, G., Johnson, S.C.: Predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population. NeuroImage 55(2), 574–589 (2011)
Erhan, D., Bengio, Y., Courville, A., Manzagol, P.A., Vincent, P., Bengio, S.: Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research 11, 625–660 (2010)
Caruana, R.: Multitask learning: A knowledge-based source of inductive bias. Machine Learning 28, 41–75 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Li, F., Tran, L., Thung, KH., Ji, S., Shen, D., Li, J. (2014). Robust Deep Learning for Improved Classification of AD/MCI Patients. In: Wu, G., Zhang, D., Zhou, L. (eds) Machine Learning in Medical Imaging. MLMI 2014. Lecture Notes in Computer Science, vol 8679. Springer, Cham. https://doi.org/10.1007/978-3-319-10581-9_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-10581-9_30
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10580-2
Online ISBN: 978-3-319-10581-9
eBook Packages: Computer ScienceComputer Science (R0)