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Book Chapter

Kernel Methods in Medical Imaging

MPS-Authors
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Charpiat,  G
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Hofmann,  M
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Schölkopf,  B
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

Charpiat, G., Hofmann, M., & Schölkopf, B. (2015). Kernel Methods in Medical Imaging. In N. Paragios, J. Duncan, & N. Ayache (Eds.), Handbook of Biomedical Imaging: Methodologies and Clinical Research (pp. 63-81). Boston, MA, USA: Springer.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002A-47CB-C
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.