Permutation Tests for Classification: Towards Statistical Significance in Image-Based Studies

  • Polina Golland
  • Bruce Fischl
Conference paper

DOI: 10.1007/978-3-540-45087-0_28

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2732)
Cite this paper as:
Golland P., Fischl B. (2003) Permutation Tests for Classification: Towards Statistical Significance in Image-Based Studies. In: Taylor C., Noble J.A. (eds) Information Processing in Medical Imaging. IPMI 2003. Lecture Notes in Computer Science, vol 2732. Springer, Berlin, Heidelberg

Abstract

Estimating statistical significance of detected differences between two groups of medical scans is a challenging problem due to the high dimensionality of the data and the relatively small number of training examples. In this paper, we demonstrate a non-parametric technique for estimation of statistical significance in the context of discriminative analysis (i.e., training a classifier function to label new examples into one of two groups). Our approach adopts permutation tests, first developed in classical statistics for hypothesis testing, to estimate how likely we are to obtain the observed classification performance, as measured by testing on a hold-out set or cross-validation, by chance. We demonstrate the method on examples of both structural and functional neuroimaging studies.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Polina Golland
    • 1
  • Bruce Fischl
    • 2
  1. 1.Artificial Intelligence LaboratoryMassachusetts Institute of TechnologyCambridge
  2. 2.Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General Hospital, Harvard Medical SchoolBoston

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