Abstract
As part of the AIDTumour research project, the analysis of MRS data corresponding to various tumour pathologies is used to assist expert diagnosis. The high dimensionality of the MR spectra might obscure atypical aspects of the data that would jeopardize their automated classification and, as a result, the process of computer-based diagnostic assistance. In this paper, we put forward a method to overcome this potential problem that combines automatic outlier detection, visualization through dimensionality reduction, and expert opinion.
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References
Artificial Intelligence Decision Tools for Tumour diagnosis research project, http://www.lsi.upc.edu/~websoco/AIDTumour
Julià-Sapé, M., et al.: A Multi-Centre, Web-Accessible and Quality Control-Checked Database of in Vivo MR Spectra of Brain Tumour Patients. Magn. Reson. Mater. Phy. 19, 22–33 (2006)
Vellido, A., Lisboa, P.J.G.: Handling Outliers in Brain Tumour MRS Data Analysis through Robust Topographic Mapping. Comput. Biol. Med. 36, 1049–1063 (2006)
Sammon Jr., J.W.: A nonlinear mapping for data structure analysis. IEEE T. Comput. C-18, 401–409 (1969)
Bishop, C.M., Svensén, M., Williams, C.K.I.: The Generative Topographic Mapping. Neural Comput. 10(1), 215–234 (1998)
KING visualization software, http://kinemage.biochem.duke.edu/software/king.php
Peel, D., McLachlan, G.J.: Robust mixture modelling using the t distribution. Stat. Comput. 10, 339–348 (2000)
Dickersin, K., Straus, S.E., Bero, L.A.: Evidence Based Medicine: Increasing, not Dictating. Choice. Brit. Med. J. 334(supl.1), s10 (2007)
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Vellido, A., Julià-Sapé, M., Romero, E., Arús, C. (2008). Exploratory Characterization of Outliers in a Multi-centre 1H-MRS Brain Tumour Dataset. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85565-1_24
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DOI: https://doi.org/10.1007/978-3-540-85565-1_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85564-4
Online ISBN: 978-3-540-85565-1
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