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

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http://link.springer.com/content/pdf/10.10072F978-0-387-09749-7.pdf (Publisher version)
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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: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-0-387-09749-7_4
BibTex Citekey: CharpiatHS2014
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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