Handling outliers in brain tumour MRS data analysis through robust topographic mapping.
Uncertainty is inherent in medical decision making and poses a challenge for intelligent technologies. This paper focuses on magnetic resonance spectra (MRS) for discrimination of brain tumour types and grades. Modelling of this type of high-dimensional data is commonly affected by uncertainty caused by the presence of outliers. Multivariate data clustering and visualization of MRS data is proposed using the GTM framework with basis functions comprising Student t-distributions in order to minimize the negative impact on the model from outliers. The effectiveness of this model on the MRS data is demonstrated empirically.
Vellido
Alfredo
A
Universitat Politècnica de Catalunya (UPC), Soft Computing Group, C. Jordi Girona, 1-3, 08034, Barcelona, Spain. avellido@lsi.upc.edu
Lisboa
Paulo J G
PJ
D001921:Brain / Q000473:pathology; D001931:Brain Mapping; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000453:epidemiology / Q000473:pathology; D016000:Cluster Analysis; D003625:Data Collection / Q000706:statistics & numerical data; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000706:statistics & numerical data*; D003937:Diagnosis, Differential; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000706:statistics & numerical data*; D008432:Mathematical Computing; D015233:Models, Statistical; D015999:Multivariate Analysis; D016311:Outliers, DRG / Q000706:statistics & numerical data*; D011379:Prognosis; D035501:Uncertainty