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  Kernel Methods in Medical Imaging

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.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002A-47CB-C Version Permalink: http://hdl.handle.net/21.11116/0000-0000-FAB3-C
Genre: Book Chapter

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 Creators:
Charpiat, G1, Author              
Hofmann, M1, Author              
Schölkopf, B1, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 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.

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 Dates: 2015
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1007/978-0-387-09749-7_4
BibTex Citekey: CharpiatHS2014
 Degree: -

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Title: Handbook of Biomedical Imaging: Methodologies and Clinical Research
Source Genre: Book
 Creator(s):
Paragios, N., Editor
Duncan, J., Editor
Ayache, N., Editor
Affiliations:
-
Publ. Info: Boston, MA, USA : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 63 - 81 Identifier: ISBN: 978-0-387-09748-0