Biomedical Imaging Modality Classification Using Bags of Visual and Textual Terms with Extremely Randomized Trees: Report of ImageCLEF 2010 Experiments

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%).

[1]  Frédéric Jurie,et al.  Randomized Clustering Forests for Image Classification , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[3]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[4]  E. M. Hartwell.  Boston , 1906 .

[5]  김삼묘,et al.  “Bioinformatics” 특집을 내면서 , 2000 .

[6]  P. Geurts,et al.  Inferring Regulatory Networks from Expression Data Using Tree-Based Methods , 2010, PloS one.

[7]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[8]  Raphaël Marée,et al.  Content-based Image Retrieval by Indexing Random Subwindows with Randomized Trees , 2009, IPSJ Trans. Comput. Vis. Appl..

[9]  Pierre Geurts,et al.  Proteomic mass spectra classification using decision tree based ensemble methods , 2005, Bioinform..

[10]  Louis Wehenkel,et al.  Automatic Learning Techniques in Power Systems , 1997 .

[11]  Raphaël Marée,et al.  Random subwindows for robust image classification , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[12]  P. Geurts,et al.  Random subwindows and extremely randomized trees for image classification in cell biology , 2007, BMC Cell Biology.

[13]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.