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  • Khatami, Mohammad, et al. (author)
  • BundleMAP : Anatomically localized classification, regression, and hypothesis testing in diffusion MRI
  • 2017
  • In: Pattern Recognition. - : Elsevier BV. - 0031-3203. ; 63, s. 593-600
  • Journal article (peer-reviewed)abstract
    • Diffusion MRI (dMRI) provides rich information on the white matter of the human brain, enabling insight into neurological disease, normal aging, and neuroplasticity. We present BundleMAP, an approach to extracting features from dMRI data that can be used for supervised classification, regression, and hypothesis testing. Our features are based on aggregating measurements along nerve fiber bundles, enabling visualization and anatomical interpretation. The main idea behind BundleMAP is to use the ISOMAP manifold learning technique to jointly parametrize nerve fiber bundles. We combine this idea with mechanisms for outlier removal and feature selection to obtain a practical machine learning pipeline. We demonstrate that it increases accuracy of disease detection and estimation of disease activity, and that it improves the power of statistical tests.
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Type of publication
journal article (1)
Type of content
peer-reviewed (1)
Author/Editor
Sundgren, Pia C. (1)
Schölkopf, Bernhard (1)
Schultz, Thomas (1)
Schmidt-Wilcke, Tobi ... (1)
Khatami, Mohammad (1)
Abbasloo, Amin (1)
University
Lund University (1)
Language
English (1)
Research subject (UKÄ/SCB)
Medical and Health Sciences (1)
Year
2017 (1)

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