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Semi-supervised outcome prediction for a type of human brain tumour using partially labeled MRS information

Autor
Cruz, R.; Vellido, A.
Tipus d'activitat
Article en revista
Revista
Lecture notes in computer science
Data de publicació
2009
Volum
5788
Pàgina inicial
168
Pàgina final
175
DOI
https://doi.org/10.1007/978-3-642-04394-9 Obrir en finestra nova
URL
http://www.springerlink.com/content/g2415q4402656081/ Obrir en finestra nova
Resum
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, ...
Grup de recerca
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
SOCO - Soft Computing

Participants