Improving Single-Modal Neuroimaging Based Diagnosis of Brain Disorders via Boosted Privileged Information Learning Framework

  • Xiao Zheng
  • Jun ShiEmail author
  • Shihui Ying
  • Qi Zhang
  • Yan Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10019)


In clinical practice, it is more prevalent to use only a single-modal neuroimaging for diagnosis of brain disorders, such as structural magnetic resonance imaging. A neuroimaging dataset generally suffers from the small-sample-size problem, which makes it difficult to train a robust and effective classifier. The learning using privileged information (LUPI) is a newly proposed paradigm, in which the privileged information is available only at the training phase to provide additional information about training samples, but unavailable in the testing phase. LUPI can effectively help construct a better predictive rule to promote classification performance. In this paper, we propose to apply LUPI for the single-modal neuroimaging based diagnosis of brain diseases along with multi-modal training data. Moreover, a boosted LUPI framework is developed, which performs LUPI-based random subspace learning and then ensembles all the LUPI classifiers with the multiple kernel boosting (MKB) algorithm. The experimental results on two neuroimaging datasets show that LUPI-based algorithms are superior to the traditional classifier models for single-modal neuroimaging based diagnosis of brain disorders, and the proposed boosted LUPI framework achieves best performance.


Support Vector Machine Mild Cognitive Impairment Weak Classifier Multiple Kernel Multiple Kernel Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is supported by the National Natural Science Foundation of China (61471231, 61401267, 11471208, 61201042, 61471245, U1201256), the Projects of Guangdong R/D Foundation and the New Technology R/D projects of Shenzhen City.


  1. 1.
    Zhang, D.Q., Wang, Y.P., Zhou, L.P., Yuan, H., Shen, D.G.: ADNI: multimodal classification of Alzheimer’s disease and mild cognitive impairment. NeuroImage 55(3), 856–867 (2011)CrossRefGoogle Scholar
  2. 2.
    Mwangi, B., Tian, T.S., Soares, J.C.: A review of feature reduction techniques in neuroimaging. Neuroinformatics 12(2), 229–244 (2014)CrossRefGoogle Scholar
  3. 3.
    Filipovych, R., Davatzikos, C.: Semi-supervised pattern classification of medical images: application to mild cognitive impairment (MCI). NeuroImage 55(3), 1109–1119 (2011)CrossRefGoogle Scholar
  4. 4.
    Cheng, B., Liu, M.X., Zhang, D.Q., Munsell, B.C., Shen, D.G.: ADNI: domain transfer learning for MCI conversion prediction. IEEE Trans. Biomed. Eng. 62(7), 1805–1817 (2015)CrossRefGoogle Scholar
  5. 5.
    Vapnik, V., Vashist, A.: A new learning paradigm: learning using privileged information. Neural Netw. 22, 544–557 (2009)CrossRefzbMATHGoogle Scholar
  6. 6.
    Pechyony, D., Izmailov, R., Vashist, A., Vapnik, V.: SMO-style algorithms for learning using privileged information. In: DMIN, pp. 235–241 (2010)Google Scholar
  7. 7.
    Kuncheva, L.I., Rodríguez, J.J., Plumpton, C.O., Linden, D.E.J., Johnston, S.J.: Random subspace ensembles for fMRI classification. IEEE Trans. Med. Imaging 29(2), 531–542 (2010)CrossRefGoogle Scholar
  8. 8.
    Yang, F., Lu, H.C., Yang, M.H.: Robust visual tracking via multiple kernel boosting with affinity constraints. IEEE Trans. Circuits Syst. Video Technol. 24(2), 242–254 (2014)CrossRefGoogle Scholar
  9. 9.
    Silva, R.F., Castro, E., Gupta, C.N., Cetin, M., Arbabshirani, M., Potluru, V.K., Plis, S.M., Calhoun, V.D.: The tenth annual MLSP competition schizophrenia classification challenge. In: MLSP, pp. 1–6 (2014)Google Scholar
  10. 10.
    Jack, C.R., Bernstein, M.A., Fox, N.C., et al.: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27, 685–691 (2008)CrossRefGoogle Scholar
  11. 11.
    Li, W., Duan, L.X., Xu, D., Tsang, I.W.: Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1134–1148 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Xiao Zheng
    • 1
  • Jun Shi
    • 1
    Email author
  • Shihui Ying
    • 2
  • Qi Zhang
    • 1
  • Yan Li
    • 3
  1. 1.School of Communication and Information EngineeringShanghai UniversityShanghaiChina
  2. 2.Department of Mathematics, School of ScienceShanghai UniversityShanghaiChina
  3. 3.College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina

Personalised recommendations