Abstract
The histological grade of a brain tumor is an important indicator for choosing the treatment after resection. To facilitate objectivity and reproducibility, Iglesias et al. (1986) proposed to use a standardized protocol of 50 histological features in the grading process.
We tested the ability of Support Vector Machines (SVM), Learning Vector Quantization (LVQ) and Supervised Relevance Neural Gas (SRNG) to predict the correct grades of the 794 astrocytomas in our database. Furthermore, we discuss the stability of the procedure with respect to errors and propose a different parametrization of the metric in the SRNG algorithm to avoid the introduction of unnecessary boundaries in the parameter space.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
BARLETT, PL. and MENDELSON, S. (2002): Rademacher and Gaussian Complexities: Risk Bounds and Structural Results. Journal of Machine Learning, 3, 463-482.
COONS, SW., JOHNSON, PC., SCHEITHAUER, BW., YATES, AJ., PEARL, DK. (1997): Improving diagnostic accuracy and interobserver concordance in the classification and grading of primary gliomas. Cancer, 79, 1381-1393.
CRAMMER, K., GILAD-BACHRACH, R., NAVOT, A. and TISHBY A. (2003): Margin Analysis of the LVQ algorithm. In: Proceedings of the Fifteenth Annual Conference on Neural Information Processing Systems (NIPS). MIT Press, Cambridge, MA 462-469.
HAMMER, B., STRICKERT, M., VILLMANN, T. (2005): On the generalization ability of GRLVQ networks. Neural Processing Letters, 21(2), 109-120.
IGLESIAS, JR., PFANNKUCH, F., ARUFFO, C., KAZNER, E. and CERVÓS-NAVARRO, J. (1986): Histopathological diagnosis of brain tumors with the help of a computer: mathe-matical fundaments and practical application. Acta. Neuropathol. , 71, 130-135.
KOHONEN, T., KANGAS, J., LAAKSONEN, J. and TORKKOLA, K. (1992): LVQ-PAK: A program package for the correct application of Learning Vector Quantization algo-rithms. In: Proceedings of the International Joint Conference on Neural Networks. IEEE, Baltimore, 725-730.
KOHONEN, T. (1995): Self-Organizing Maps. Springer Verlag, Heidelberg.
VAPNIK, V. (1995): The Nature of Statistical Learning Theory. Springer Verlag, New York, NY.
VILLMANN, T., HAMMER, B. and STRICKERT, M. (2002): Supervised neural gas for learning vector quantization. In: D. Polani, J. Kim, T. Martinetz (Eds.): Fifth German Workshop on Artificial Life. IOS Press, 9-18
VILLMANN, T., SCHLEIF, F-M. and HAMMER, B. (2006): Comparison of Relevance Learning Vector Quantization with other Metric Adaptive Classification Methods.Neural Networks, 19(5), 610-622.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Röhrl, N., Iglesias-Rozas, J.R., Weidl, G. (2008). Computer Assisted Classification of Brain Tumors. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78246-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-540-78246-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78239-1
Online ISBN: 978-3-540-78246-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)