Multi-stage Diagnosis of Alzheimer’s Disease with Incomplete Multimodal Data via Multi-task Deep Learning

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10553)


Utilization of biomedical data from multiple modalities improves the diagnostic accuracy of neurodegenerative diseases. However, multi-modality data are often incomplete because not all data can be collected for every individual. When using such incomplete data for diagnosis, current approaches for addressing the problem of missing data, such as imputation, matrix completion and multi-task learning, implicitly assume linear data-to-label relationship, therefore limiting their performances. We thus propose multi-task deep learning for incomplete data, where prediction tasks that are associated with different modality combinations are learnt jointly to improve the performance of each task. Specifically, we devise a multi-input multi-output deep learning framework, and train our deep network subnet-wise, partially updating its weights based on the availability of modality data. The experimental results using the ADNI dataset show that our method outperforms the state-of-the-art methods.


  1. 1.
    Adeli-Mosabbeb, E., et al.: Robust feature-sample linear discriminant analysis for brain disorders diagnosis. In: Advances in Neural Information Processing Systems, pp. 658–666 (2015)Google Scholar
  2. 2.
    Candès, E.J., et al.: Exact matrix completion via convex optimization. Found. Comput. Math. 9(6), 717–772 (2009)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Chollet, F.: Keras (2015).
  4. 4.
    Goldberg, A., et al.: Transduction with matrix completion: three birds with one stone. Adv. Neural Inf. Process. Syst. 23, 757–765 (2010)Google Scholar
  5. 5.
    Li, F., et al.: A robust deep model for improved classification of AD/MCI patients. IEEE J. Biomed. Health Inf. 19(5), 1610–1616 (2015)CrossRefGoogle Scholar
  6. 6.
    Liu, S., et al.: Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease. IEEE Trans. Biomed. Eng. 62(4), 1132–1140 (2015)CrossRefGoogle Scholar
  7. 7.
    Thung, K.H., et al.: Neurodegenerative disease diagnosis using incomplete multi-modality data via matrix shrinkage and completion. Neuroimage 91, 386–400 (2014)CrossRefGoogle Scholar
  8. 8.
    Thung, K.H., et al.: Identification of progressive mild cognitive impairment patients using incomplete longitudinal MRI scans. Brain Struct. Funct. 221(8), 3979–3995 (2016)CrossRefGoogle Scholar
  9. 9.
    Thung, K.-H., Yap, P.-T., Adeli-M, E., Shen, D.: Joint diagnosis and conversion time prediction of progressive mild cognitive impairment (pMCI) using low-rank subspace clustering and matrix completion. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 527–534. Springer, Cham (2015). doi: 10.1007/978-3-319-24574-4_63 CrossRefGoogle Scholar
  10. 10.
    Thung, K.-H., Adeli, E., Yap, P.-T., Shen, D.: Stability-weighted matrix completion of incomplete multi-modal data for disease diagnosis. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 88–96. Springer, Cham (2016). doi: 10.1007/978-3-319-46723-8_11 CrossRefGoogle Scholar
  11. 11.
    Troyanskaya, O., et al.: Missing value estimation methods for DNA microarrays. Bioinformatics 17(6), 520–525 (2001)CrossRefGoogle Scholar
  12. 12.
    Wang, Y., Nie, J., Yap, P.-T., Shi, F., Guo, L., Shen, D.: Robust deformable-surface-based skull-stripping for large-scale studies. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6893, pp. 635–642. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-23626-6_78 CrossRefGoogle Scholar
  13. 13.
    Weiner, M.W., et al.: The Alzheimer’s disease neuroimaging initiative: a review of papers published since its inception. Alzheimer’s Dementia 9(5), e111–e194 (2013)CrossRefGoogle Scholar
  14. 14.
    Wyman, B.T., et al.: Standardization of analysis sets for reporting results from ADNI MRI data. Alzheimer’s Dementia 9(3), 332–337 (2013)CrossRefGoogle Scholar
  15. 15.
    Yu, G., et al.: Multi-task linear programming discriminant analysis for the identification of progressive MCI individuals. PLoS ONE 9(5), e96458 (2014)CrossRefGoogle Scholar
  16. 16.
    Yuan, L., et al.: Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data. NeuroImage 61(3), 622–632 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Radiology and BRICUniversity of North CarolinaChapel HillUSA

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