Institute for Molecules and Materials, Radboud University Nijmegen, Heijendaalseweg 135, 6525 AJ Nijmegen, The Netherlands.
Comput Biol Med. 2011 Feb;41(2):87-97. doi: 10.1016/j.compbiomed.2010.12.003. Epub 2011 Jan 13.
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.
为了评估通过自动特征选择技术选择的磁共振(MR)特征与构建用于鉴别诊断和组织分割的分类器的相关性,研究了两个包含脑肿瘤患者的磁共振波谱数据的数据集。使用文献和临床经验评估了自动选择的特征。结果表明,自动选择的特征中有很大一部分与文献和临床经验所知的内容相对应。我们得出结论,自动特征选择是获得相关且可能有趣的特征的有用工具,但仍需要对获得的特征进行评估。