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Modality Neutral Techniques for Brain Image Understanding

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Machine Learning and Interpretation in Neuroimaging

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7263))

Abstract

With the influx of available multi-modality neuroimaging data, the need to mine large databases for interesting features in a modality neutral way across many brain disorders is of interest. In this paper I present some examples of applying models originating in the computer vision and text mining communities to neuroimaging data which are not tuned for a particular imaging modality and are agnostic to the underlying brain disorder.

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Author information

Authors and Affiliations

  1. University of California, Irvine, USA

    David B. Keator

Authors
  1. David B. Keator
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Editor information

Editors and Affiliations

  1. Department of Radiology, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Wien, Austria

    Georg Langs

  2. Computational Biology Center, IBM T.J. Watson Research Center, 1101 Kitchawan Road, 10598, Yorktown Heights, NY, USA

    Irina Rish

  3. Max Planck Institute for Intelligent Systems, Spemannstraße 38, 72076, Tübingen, Germany

    Moritz Grosse-Wentrup

  4. Machine Learning Department, Carnegie Mellon University, 5000 Forbes Avenue, 15213-3891, Pittsburgh, PA, USA

    Brian Murphy

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Keator, D.B. (2012). Modality Neutral Techniques for Brain Image Understanding. In: Langs, G., Rish, I., Grosse-Wentrup, M., Murphy, B. (eds) Machine Learning and Interpretation in Neuroimaging. Lecture Notes in Computer Science(), vol 7263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34713-9_11

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  • DOI: https://doi.org/10.1007/978-3-642-34713-9_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34712-2

  • Online ISBN: 978-3-642-34713-9

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