Abstract
The diagnosis and prognosis of human brain tumours, especially when they are aggresive, are sensitive clinical tasks that usually require non-invasive measurement techniques. Outcome information for aggressive tumours, in particular, is usually scarce. In this paper, we aim to gauge the capability of a novel semi-supervised model, SS-Geo-GTM, to infer outcome stages from a very limited amount of available stage labels and Magnetic Resonance Spectroscopy (MRS) data corresponding to Glioblastoma, which is an aggressive tumor type. This model stems from a geodesic distance-based extension of Generative Topographic Mapping (Geo-GTM) that prioritizes neighbourhood relationships along a generated manifold embedded in the observed data space.
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Cruz-Barbosa, R., Vellido, A. (2009). Semi-supervised Outcome Prediction for a Type of Human Brain Tumour Using Partially Labeled MRS Information. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_21
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DOI: https://doi.org/10.1007/978-3-642-04394-9_21
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