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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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
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