Skip to main content
Springer Nature Link
Log in

Multi-Phase Feature Representation Learning for Neurodegenerative Disease Diagnosis

  • Conference paper
Artificial Life and Computational Intelligence (ACALCI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8955))

Abstract

Feature learning with high dimensional neuroimaging features has been explored for the applications on neurodegenerative diseases. Low-dimensional biomarkers, such as mental status test scores and cerebrospinal fluid level, are essential in clinical diagnosis of neurological disorders, because they could be simple and effective for the clinicians to assess the disorder’s progression and severity. Rather than only using the low-dimensional biomarkers as inputs for decision making systems, we believe that such low-dimensional biomarkers can be used for enhancing the feature learning pipeline. In this study, we proposed a novel feature representation learning framework, Multi-Phase Feature Representation (MPFR), with low-dimensional biomarkers embedded. MPFR learns high-level neuroimaging features by extracting the associations between the low-dimensional biomarkers and the high-dimensional neuroimaging features with a deep neural network. We validated the proposed framework using the Mini-Mental-State-Examination (MMSE) scores as a low-dimensional biomarker and multi-modal neuroimaging data as the high-dimensional neuroimaging features from the ADNI baseline cohort. The proposed approach outperformed the original neural network in both binary and ternary Alzheimer’s disease classification tasks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. 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)

    Chapter  Google Scholar 

  2. Liu, S.Q., Liu, S., Cai, W., Pujol, S., Kikinis, R., Feng, D.: Early Diagnosis of Alzheimer’s Disease with Deep Learning. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), pp. 1015–1018. IEEE (2014)

    Google Scholar 

  3. Liu, S.Q., Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., Fulham, M.J., Feng, D.: High-level Feature based PET Image Retrieval with Deep Learning Architecture. Journal of Nuclear Medicine 55(Supple. 1), 2018 (2014)

    Google Scholar 

  4. Fischl, B., Dale, A.M.: Measuring the Thickness of the Human Cerebral Cortex from Magnetic Resonance Images. Proceedings of the National Academy of Sciences 97(20), 11050–11055 (2000)

    Article  Google Scholar 

  5. Schaer, M., Cuadra, M.B., Tamarit, L., Lazeyras, F., Eliez, S., Thiran, J.P.: A Surface-Based Approach to Quantify Local Cortical Gyrification. IEEE Transactions on Medical Imaging 27(2), 161–170 (2008)

    Article  Google Scholar 

  6. Liu, S., Cai, W., Wen, L., Eberl, S., Fulham, M.J., Feng, D.: A Robust Volumetric Feature Extraction Approach for 3D Neuroimaging Retrieval. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5657–5660. IEEE (2010)

    Google Scholar 

  7. Cai, W., Liu, S., Wen, L., Eberl, S., Fulham, M., Feng, D.: 3D Neurological Image Retrieval with Localized Pathology-Centric CMRGlc Patterns. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 3201–3204 (2010)

    Google Scholar 

  8. Liu, S., Cai, W., Wen, L., Eberl, S., Fulham, M.J., Feng, D.: Localized Functional Neuroimaging Retrieval using 3D Discrete Curvelet Transform. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), pp. 1877–1880. IEEE (2011)

    Google Scholar 

  9. Cash, D.M., Melbourne, A., Modat, M., Cardoso, M.J., Clarkson, M.J., Fox, N.C., Ourselin, S.: Cortical folding analysis on patients with alzheimer’s disease and mild cognitive impairment. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 289–296. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  10. Liu, S., Song, Y., Cai, W., Pujol, S., Kikinis, R., Wang, X., Feng, D.: Multifold Bayesian Kernelization in Alzheimer’s Diagnosis. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 303–310. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  11. Liu, S., Cai, W., Wen, L., Feng, D.: Neuroimaging Biomarker Based Prediction of Alzheimer’S Disease Severity with Optimized Graph Construction. In: IEEE 10th International Symposium on Biomedical Imaging (ISBI), pp. 1336–1339. IEEE (2013)

    Google Scholar 

  12. Cai, W., Liu, S., Song, Y., Pujol, S., Kikinis, R., Feng, D.: A 3D Difference of Gaussian based Lesion Detector for Brain PET. In: 2014 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), pp. 677–680. IEEE (2014)

    Google Scholar 

  13. Zhang, D., Wang, Y., Zhou, L., Yuan, H., Shen, D.: Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment. NeuroImage 55(3), 856 (2011)

    Article  Google Scholar 

  14. Wang, Y., Fan, Y., Bhatt, P., Davatzikos, C.: High-Dimensional Pattern Regression Using Machine Learning: From Medical Images to Continuous Clinical Variables. NeuroImage 50(4), 1519 (2010)

    Article  Google Scholar 

  15. Zhang, D., Shen, D.: Multi-Modal Multi-Task Learning for Joint Prediction of Multiple Regression and Classification Variables in Alzheimer’s Disease. NeuroImage 59(2), 895 (2012)

    Article  Google Scholar 

  16. Ngiam, J., Coates, A., Lahiri, A., Prochnow, B., Ng, A., Le, Q.V.: On Optimization Methods for Deep Learning. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 265–272 (2011)

    Google Scholar 

  17. Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H., et al.: Greedy Layer-Wise Training of Deep Networks. Advances in Neural Information Processing Systems 19, 153 (2007)

    Google Scholar 

  18. Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.: Multimodal Deep Learning. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 689–696 (2011)

    Google Scholar 

  19. Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Finet, J., Fillion-Robin, J.C., Pujol, S., et al.: 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network. Magnetic Resonance Imaging 30(9), 1323–1341 (2012)

    Article  Google Scholar 

  20. Jack, C.R., Bernstein, M.A., Fox, N.C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., et al.: The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI Methods. Journal of Magnetic Resonance Imaging 27(4), 685–691 (2008)

    Article  Google Scholar 

  21. Jagust, W.J., Bandy, D., Chen, K., Foster, N.L., Landau, S.M., Mathis, C.A., Price, J.C., et al.: The Alzheimer’s Disease Neuroimaging Initiative Positron Emission Tomography Core. Alzheimer’s & Dementia 6(3), 221–229 (2010)

    Article  Google Scholar 

  22. Jenkinson, M., Bannister, P., Brady, M., Smith, S.: Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images. Neuroimage 17(2), 825–841 (2002)

    Article  Google Scholar 

  23. Mazziotta, J., Toga, A., Evans, A., Fox, P., Lancaster, J., Zilles, K., Woods, R., et al.: A Probabilistic Atlas and Reference System for the Human Brain: International Consortium for Brain Mapping (ICBM). Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 356(1412), 1293–1322 (2001)

    Article  Google Scholar 

  24. Schnabel, J.A., Rueckert, D., Quist, M., Blackall, J.M., Castellano-Smith, A.D., Hartkens, T., Penney, G.P., Hall, W.A., Liu, H., Truwit, C.L., Gerritsen, F.A., Hill, D.L.G., Hawkes, D.J.: A Generic Framework for Non-rigid Registration Based on Non-uniform Multi-level Free-Form Deformations. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 573–581. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  25. Heckemann, R.A., Keihaninejad, S., Aljabar, P., Gray, K.R., Nielsen, C., Rueckert, D., Hajnal, J.V., Hammers, A.: Automatic Morphometry in Alzheimer’s Disease and Mild Cognitive Impairment. Neuroimage 56(4), 2024–2037 (2011)

    Article  Google Scholar 

  26. Liu, S., Cai, W., Song, Y., Pujol, S., Kikinis, R., Wen, L., Feng, D.: Localized Sparse Code Gradient in Alzheimer’s Disease Staging. In: 2013 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5398–5401. IEEE (2013)

    Google Scholar 

  27. Che, H., Liu, S., Cai, W., Pujol, S., Kikinis, R., Feng, D.: Co-neighbor Multi-View Spectral Embedding for Medical Content-based Retrieval. In: 2014 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), pp. 911–914. IEEE (2014)

    Google Scholar 

  28. Batchelor, P.G., Castellano Smith, A.D., Hill, D.L.G., Hawkes, D.J., Cox, T.C.S., Dean, A.: Measures of Folding Applied to the Development of the Human Fetal Brain. IEEE Transactions on Medical Imaging 21(8), 953–965 (2002)

    Article  Google Scholar 

  29. Liu, S., Zhang, L., Cai, W., Song, Y., Wang, Z., Wen, L., Feng, D.: A Supervised Multiview Spectral Embedding Method for Neuroimaging Classification. In: 2013 IEEE International Conference on Image Processing (ICIP), pp. 601–605. IEEE (2013)

    Google Scholar 

  30. Liu, S., Cai, W., Wen, L., Eberl, S., Fulham, M.J., Feng, D.: Generalized Regional Disorder-Sensitive-Weighting Scheme for 3D Neuroimaging Retrieval. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 7009–7012. IEEE (2011)

    Google Scholar 

  31. Zou, H., Hastie, T.: Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67(2), 301–320 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  32. Liu, S., Cai, W., Wen, L., Feng, D.: Multi-Channel Brain Atrophy Pattern Analysis in Neuroimaging Retrieval. In: 2013 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), pp. 206–209. IEEE (2013)

    Google Scholar 

  33. Liu, S., Cai, W., Wen, L., Feng, D.D., Pujol, S., et al.: Multi-Channel Neurodegenerative Pattern Analysis and Its Application in Alzheimer’s Disease Characterization. Computerized Medical Imaging and Graphics 38(4), 436–444 (2014)

    Article  Google Scholar 

  34. Chang, C.C., Lin, C.J.: LIBSVM: A Library for Support Vector Machines. ACM Trans. Intell. Syst. Technol. 27(3), 1–27 (2011)

    Article  Google Scholar 

  35. Liu, Y.F.Y., Zheng, Y.: One-Against-All Multi-Class SVM Classification Using Reliability Measures. In: Neural Networks (IJCNN 2005), IEEE International Joint Conference on. Volume 2. (2005) 849–854

    Google Scholar 

  36. Bergstra, J., Bengio, Y.: Random Search for Hyper-Parameter Optimization. The Journal of Machine Learning Research 13, 281–305 (2012)

    MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. BMIT Research Group, School of Information Technologies, University of Sydney, NSW, Australia

    Siqi Liu, Sidong Liu, Weidong Cai & David Dagan Feng

  2. Surgical Planning Laboratory, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, USA

    Sidong Liu, Weidong Cai, Sonia Pujol & Ron Kikinis

  3. Med-X Research Institute, Shanghai Jiaotong University, Shanghai, China

    David Dagan Feng

Authors
  1. Siqi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sidong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weidong Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sonia Pujol
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ron Kikinis
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. David Dagan Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Electrical Engineering and Computer Science, The University of Newcastle, 2308, Callaghan, NSW, Australia

    Stephan K. Chalup

  2. School of Computer Science and Engineering, The University of New South Wales, 2052, Sydney, NSW, Australia

    Alan D. Blair

  3. Faculty of Business, Bond University, 4226, Robina, QLD, Australia

    Marcus Randall

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., Feng, D.D. (2015). Multi-Phase Feature Representation Learning for Neurodegenerative Disease Diagnosis. In: Chalup, S.K., Blair, A.D., Randall, M. (eds) Artificial Life and Computational Intelligence. ACALCI 2015. Lecture Notes in Computer Science(), vol 8955. Springer, Cham. https://doi.org/10.1007/978-3-319-14803-8_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-14803-8_27

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14802-1

  • Online ISBN: 978-3-319-14803-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics