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Biomedical Imaging Modality Classification Using Bags of Visual and Textual Terms with Extremely Randomized Trees: Report of ImageCLEF 2010 Experiments
Marée, Raphaël; Stern, Olivier; Geurts, Pierre
2010In CLEF 2010: Padua, Italy - Notebook Papers/LABs/Workshops
 

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Abstract :
[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%).
Research center :
Giga-Systems Biology and Chemical Biology - ULiège
Disciplines :
Computer science
Author, co-author :
Marée, Raphaël  ;  Université de Liège - ULiège > GIGA-Management : Plateforme bioinformatique
Stern, Olivier ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Geurts, Pierre ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Biomedical Imaging Modality Classification Using Bags of Visual and Textual Terms with Extremely Randomized Trees: Report of ImageCLEF 2010 Experiments
Publication date :
2010
Event name :
CLEF 2010 LABs and Workshops
Event date :
22-23 September 2010
Audience :
International
Main work title :
CLEF 2010: Padua, Italy - Notebook Papers/LABs/Workshops
Funders :
FEDER - Fonds Européen de Développement Régional [BE]
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]
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