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.

[1]  Geoffrey E. Hinton,et al.  GTM through time , 1997 .

[2]  Paulo J. G. Lisboa,et al.  Outstanding Issues for Clinical Decision Support with Neural Networks , 2000, ANNIMAB.

[3]  P. Lisboa,et al.  Characterizing and Segmenting the Online Customer Market Using Neural Networks , 2002 .

[4]  Shy Shoham,et al.  Robust clustering by deterministic agglomeration EM of mixtures of multivariate t-distributions , 2002, Pattern Recognit..

[5]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[6]  Juha Vesanto,et al.  SOM-based data visualization methods , 1999, Intell. Data Anal..

[7]  Rajesh N. Davé,et al.  Robust clustering methods: a unified view , 1997, IEEE Trans. Fuzzy Syst..

[8]  Peter Tiño,et al.  Hierarchical GTM: Constructing Localized Nonlinear Projection Manifolds in a Principled Way , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Mark A. Girolami.  Latent variable models for the topographic organisation of discrete and strictly positive data , 2002, Neurocomputing.

[10]  Miguel Á. Carreira-Perpiñán,et al.  Reconstruction of Sequential Data with Probabilistic Models and Continuity Constraints , 1999, NIPS.

[11]  W. Baxt.  Application of artificial neural networks to clinical medicine , 1995, The Lancet.

[12]  Michel Verleysen,et al.  Flexible and Robust Bayesian Classification by Finite Mixture Models , 2004, ESANN.

[13]  Christopher M. Bishop,et al.  GTM: The Generative Topographic Mapping , 1998, Neural Computation.

[14]  Christopher M. Bishop,et al.  Developments of the generative topographic mapping , 1998, Neurocomputing.

[15]  Yi Sun,et al.  GTM-based data visualisation with incomplete data , 2001 .

[16]  B. Ripley,et al.  Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.

[17]  Geoffrey J. McLachlan,et al.  Robust mixture modelling using the t distribution , 2000, Stat. Comput..

[18]  Paulo J. G. Lisboa,et al.  Functional topographic mapping for robust handling of outliers in brain tumour data , 2005, ESANN.

[19]  Christopher M. Bishop,et al.  Robust Bayesian Mixture Modelling , 2005, ESANN.

[20]  P. Szczepaniak,et al.  E-Commerce and Intelligent Methods , 2002 .

[21]  Alfredo Vellido Alcacena.  Generative topographic mapping as a constrained mixture of student t-distributions: theoretical developments , 2004 .

[22]  Juha Vesanto,et al.  Distance Matrix Based Clustering of the Self-Organizing Map , 2002, ICANN.

[23]  Paulo J. G. Lisboa,et al.  Robust methodology for the discrimination of brain tumours from in vivo magnetic resonance spectra , 2000 .

[24]  Michael I. Jordan,et al.  Supervised learning from incomplete data via an EM approach , 1993, NIPS.

[25]  W. El-Deredy,et al.  Pattern recognition approaches in biomedical and clinical magnetic resonance spectroscopy: a review , 1997, NMR in biomedicine.

[26]  Guido Gerig,et al.  A brain tumor segmentation framework based on outlier detection , 2004, Medical Image Anal..

[27]  Paulo J. G. Lisboa,et al.  A review of evidence of health benefit from artificial neural networks in medical intervention , 2002, Neural Networks.

[28]  Richard Baumgartner,et al.  Mapping high-dimensional data onto a relative distance plane - an exact method for visualizing and characterizing high-dimensional patterns , 2004, J. Biomed. Informatics.

[29]  Lars Niklasson,et al.  Artificial Neural Networks in Medicine and Biology , 2000, Perspectives in Neural Computing.

[30]  Paulo J. G. Lisboa,et al.  Selective smoothing of the generative topographic mapping , 2003, IEEE Trans. Neural Networks.

[31]  Hong Yan,et al.  Signal processing for magnetic resonance imaging and spectroscopy , 2002 .

[32]  W El-Deredy,et al.  Tumour grading from magnetic resonance spectroscopy: a comparison of feature extraction with variable selection , 2003, Statistics in medicine.