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Unsupervised 3-D Feature Learning for Mild Traumatic Brain Injury

  • Po-Yu KaoEmail author
  • Eduardo Rojas
  • Jefferson W. Chen
  • Angela Zhang
  • B. S. Manjunath
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10154)

Abstract

We present an unsupervised three-dimensional feature clustering algorithm to gather the mTOP2016 challenge data into 3 groups. We use the brain MR-T1, diffusion tensor fractional anisotropy, and diffusion tensor mean diffusivity images provided by the mTOP2016 competition. A distance-based size constraint method for data clustering is used. The proposed approach achieves 0.267 adjusted rand index and 0.3556 homogeneity score within the 15 labeled subjects, corresponding to 10 correctly classified data items. Based on visual exploration of the data, we believe that a localized analysis of the lesion regions, using the computed tractography data, is a promising direction to pursue.

Keywords

Fractional Anisotropy Feature Representation Mild Traumatic Brain Injury Adjusted Rand Index Feature Kernel 
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.

Notes

Acknowledgments

This research was partially supported by HD059217 from the National Institutes of Health.

References

  1. 1.
    Coates, A., Lee, H., Ng, A.Y.: An analysis of single-layer networks in unsupervised feature learning. Ann. Arbor 1001(48109), 2 (2010)Google Scholar
  2. 2.
    Coates, A., Ng, A.Y.: Learning feature representations with K-means. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 561–580. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-35289-8_30 CrossRefGoogle Scholar
  3. 3.
    Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2(1), 193–218 (1985)CrossRefzbMATHGoogle Scholar
  5. 5.
    Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images (2009)Google Scholar
  6. 6.
    Olshausen, B.A.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583), 607–609 (1996)CrossRefGoogle Scholar
  7. 7.
    Rosenberg, A., Hirschberg, J.: V-Measure: a conditional entropy-based external cluster evaluation measure. In: EMNLP-CoNLL, vol. 7 (2007)Google Scholar
  8. 8.
    Zhu, S., Wang, D., Li, T.: Data clustering with size constraints. Knowl. Based Syst. 23(8), 883–889 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Po-Yu Kao
    • 1
    Email author
  • Eduardo Rojas
    • 1
  • Jefferson W. Chen
    • 2
  • Angela Zhang
    • 1
  • B. S. Manjunath
    • 1
  1. 1.University of California, Santa BarbaraSanta BarbaraUSA
  2. 2.University of California, IrvineIrvineUSA

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