Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

Taylor, J.C. orcid.org/0000-0003-3403-1668 and fenner, J. (2017) Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification? European Journal of Nuclear Medicine and Molecular Imaging Physics, 4 (29). ISSN 1619-7070

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Copyright, Publisher and Additional Information: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Keywords: Parkinson’s disease; 123I-FP; DaTSCAN; Semi-quantification; Machine learning; Support vector machine
Dates:
  • Published: 29 November 2017
  • Accepted: 21 November 2017
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Department of Cardiovascular Science (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 14 Dec 2017 16:26
Last Modified: 14 Dec 2017 16:26
Published Version: https://doi.org/10.1186/s40658-017-0196-1
Status: Published
Publisher: Springer Verlag
Refereed: Yes
Identification Number: https://doi.org/10.1186/s40658-017-0196-1

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