publication . Preprint . 2016

Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network

Hosseini-Asl, Ehsan; Gimel'farb, Georgy; El-Baz, Ayman;
Open Access English
  • Published: 02 Jul 2016
Early diagnosis, playing an important role in preventing progress and treating the Alzheimer's disease (AD), is based on classification of features extracted from brain images. The features have to accurately capture main AD-related variations of anatomical brain structures, such as, e.g., ventricles size, hippocampus shape, cortical thickness, and brain volume. This paper proposes to predict the AD with a deep 3D convolutional neural network (3D-CNN), which can learn generic features capturing AD biomarkers and adapt to different domain datasets. The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations...
free text keywords: Computer Science - Learning, Quantitative Biology - Neurons and Cognition, Statistics - Machine Learning
Funded by
  • Funder: Canadian Institutes of Health Research (CIHR)
NIH| Alzheimers Disease Neuroimaging Initiative
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U01AG024904-01
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47 references, page 1 of 4

[4] S. Plis, D. Hjelm, R. Salakhutdinov, E. Allen, H. Bockholt, J. Long, H. Johnson, J. Paulsen, J. Turner, and V. Calhoun, “Deep learning for neuroimaging: a validation study,” Frontiers in Neuroscience, vol. 8, 2014.

[5] T. Chen, I. Goodfellow, and J. Shlens, “Net2Net: Accelerating learning via knowledge transfer,” arXiv:1511.05641 [cs.LG], 2015.

[6] M. Long and J. Wang, “Learning transferable features with deep adaptation networks,” arXiv:1502.02791 [cs.LG], 2015.

[7] J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” in Advances in Neural Information Processing Systems, 2014, pp. 3320-3328. [OpenAIRE]

on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease,” Alzheimer's & Dementia, vol. 7, no. 3, pp. 257-262, 2011.

[9] G. McKhann, D. Knopman, H. Chertkow, B. Hyman, C. Jack, C. Kawas, W. Klunk, W. Koroshetz, J. Manly, R. Mayeux et al., “The diagnosis of dementia due to Alzheimers disease: Recommendations from the National Institute on Aging-Alzheimers Association workgroups on diagnostic guidelines for Alzheimer's disease,” Alzheimer's & Dementia, vol. 7, no. 3, pp. 263- 269, 2011. [OpenAIRE]

[10] R. Cuingnet, E. Gerardin, J. Tessieras, G. Auzias, S. Lehe´ricy, M. Habert, M. Chupin, H. Benali, O. Colliot, A. D. N. Initiative et 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. [OpenAIRE]

[11] 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,” Journal of Alzheimer's Disease, vol. 41, no. 3, pp. 685-708, 2014. [OpenAIRE]

[12] M. Sabuncu and E. Konukoglu, “Clinical Prediction from Structural Brain MRI Scans: A Large-Scale Empirical Study,” Neuroinformatics, vol. 13, no. 1, pp. 31- 46, 2015.

[13] C. Jack, D. Knopman, W. Jagust, R. Petersen, M. Weiner, P. Aisen, L. Shaw, P. Vemuri, H. Wiste, S. Weigand et al., “Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers,” The Lancet Neurology, vol. 12, no. 2, pp. 207-216, 2013.

[14] C.-Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu, “Deeply-supervised nets,” arXiv:1409.5185 [stat.ML, cs.LG, cs.NE], 2014.

[15] S. Klo¨ppel, C. Stonnington, C. Chu, B. Draganski, R. Scahill, J. Rohrer, N. Fox, C. Jack, J. Ashburner, and R. Frackowiak, “Automatic classification of MR scans in Alzheimer's disease,” Brain, vol. 131, no. 3, pp. 681- 689, 2008.

[16] Y. Fan, D. Shen, R. Gur, R. Gur, and C. Davatzikos, “COMPARE: classification of morphological patterns using adaptive regional elements,” IEEE Trans. Med. Imag., vol. 26, no. 1, pp. 93-105, 2007.

[8] C. Jack, M. Albert, D. Knopman, G. McKhann, R. Sperling, M. Carrillo, B. Thies, and C. Phelps, “Introduction to the recommendations from the National Institute

[17] J. Lerch, J. Pruessner, A. Zijdenbos, D. Collins, S. Teipel, H. Hampel, and A. Evans, “Automated cortical thickness measurements from MRI can accurately separate Alzheimer's patients from normal elderly controls,” Neurobiology of Aging, vol. 29, no. 1, pp. 23-30, 2008.

47 references, page 1 of 4
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