| On the relevance of automatically selected single-voxel MRS and multimodal MRI and MRSI features for brain tumour differentiation. | |
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MedLine Citation:
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PMID: 21236418 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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In order to evaluate the relevance of magnetic resonance (MR) features selected by automatic feature selection techniques to build classifiers for differential diagnosis and tissue segmentation two data sets containing MR spectroscopy data from patients with brain tumours were investigated. The automatically selected features were evaluated using literature and clinical experience. It was observed that a significant part of the automatically selected features correspond to what is known from the literature and clinical experience. We conclude that automatic feature selection is a useful tool to obtain relevant and possibly interesting features, but evaluation of the obtained features remains necessary. |
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Authors:
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Geert J Postma; Jan Luts; Albert J Idema; Margarida Julià-Sapé; Angel Moreno-Torres; Witek Gajewicz; Johan A K Suykens; Arend Heerschap; Sabine Van Huffel; Lutgarde M C Buydens |
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Publication Detail:
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Type: Journal Article Date: 2011-01-13 |
Journal Detail:
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Title: Computers in biology and medicine Volume: 41 ISSN: 1879-0534 ISO Abbreviation: Comput. Biol. Med. Publication Date: 2011 Feb |
Date Detail:
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Created Date: 2011-01-24 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 1250250 Medline TA: Comput Biol Med Country: United States |
Other Details:
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Languages: eng Pagination: 87-97 Citation Subset: IM |
Copyright Information:
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Copyright © 2010 Elsevier Ltd. All rights reserved. |
Affiliation:
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Institute for Molecules and Materials, Radboud University Nijmegen, Heijendaalseweg 135, 6525 AJ Nijmegen, The Netherlands. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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