Reference : Biomedical Imaging Modality Classification Using Bags of Visual and Textual Terms wit...
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/82992
Biomedical Imaging Modality Classification Using Bags of Visual and Textual Terms with Extremely Randomized Trees: Report of ImageCLEF 2010 Experiments
English
Marée, Raphaël mailto [Université de Liège - ULg > > GIGA-Management : Plateforme bioinformatique >]
Stern, Olivier mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Geurts, Pierre mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
2010
CLEF Notebook Papers/LABs/Workshops
No
International
978-88-904810-0-0
Padua
Italy
[en] In this paper we describe our experiments related to the ImageCLEF 2010
medical modality classification task using extremely randomized trees.
Our best run combines bags of textual and visual features. It yields
90% recognition rate and ranks 6th among 45 runs (ranging from
94% downto 12%).
Giga-Systems Biology and Chemical Biology
Fonds Européen de Développement Régional - FEDER ; Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
http://hdl.handle.net/2268/82992

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