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Kernel Methods in Medical Imaging

  • G. CharpiatEmail author
  • M. Hofmann
  • B. Schölkopf

Abstract

We introduce machine learning techniques, more specifically kernel methods, and show how they can be used for medical imaging. After a tutorial presentation of machine learning concepts and tools, including Support Vector Machine (SVM), kernel ridge regression and kernel PCA, we present an application of these tools to the prediction of Computed Tomography (CT) images based on Magnetic Resonance (MR) images.

Keywords

Support Vector Machine Feature Space Kernel Method Reproduce Kernel Hilbert Space Training Point 
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.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Max Planck Institute for Biological CyberneticsTuebingenGermany

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