Overview of the ImageCLEFmed 2006 Medical Retrieval and Medical Annotation Tasks

This paper describes the medical image retrieval and annotation tasks of ImageCLEF 2006. Both tasks are described with respect to goals, databases, topics, results, and techniques. The ImageCLEFmed retrieval task had 12 participating groups (100 runs). Most runs were automatic, with only a few manual or interactive. Purely textual runs were in the majority compared to purely visual runs but most were mixed, using visual and textual information. None of the manual or interactive techniques were significantly better than automatic runs. The best-performing systems used visual and textual techniques combined, but combinations of visual and textual features often did not improve performance. Purely visual systems only performed well on visual topics. The medical automatic annotation used a larger database of 10,000 training images from 116 classes, up from 9,000 images from 57 classes in 2005. Twelve groups submitted 28 runs. Despite the larger number of classes, results were almost as good as in 2005 which demonstrates a clear improvement in performance. The best system of 2005 would have received a position in the middle in 2006.

[1]  J. Wallis,et al.  An Internet-based nuclear medicine teaching file. , 1995, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[2]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Jacques Savoy Report on CLEF-2001 Experiments , 2001, CLEF.

[4]  Ee-Peng Lim,et al.  Hierarchical text classification and evaluation , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[5]  K. Glatz-Krieger,et al.  Webbasierte Lernwerkzeuge für die Pathologie , 2003, Der Pathologe.

[6]  S. Uijtdehaage,et al.  Introducing HEAL: The Health Education Assets Library , 2003, Academic medicine : journal of the Association of American Medical Colleges.

[7]  Michael Kohnen,et al.  The IRMA code for unique classification of medical images , 2003, SPIE Medical Imaging.

[8]  M. Gosselin,et al.  Contrast Dynamics During CT Pulmonary Angiogram: Analysis of an Inspiration Associated Artifact , 2004, Journal of thoracic imaging.

[9]  O. Ratib,et al.  Casimage Project: A Digital Teaching Files Authoring Environment , 2004, Journal of thoracic imaging.

[10]  Mark Sanderson,et al.  The CLEF 2004 Cross-Language Image Retrieval Track , 2004, CLEF.

[11]  Mark Sanderson,et al.  The CLEF Cross Language Image Retrieval Track (ImageCLEF) 2004 , 2004, CLEF.

[12]  Hermann Ney,et al.  Classification of Medical Images using Non-linear Distortion Models , 2004, Bildverarbeitung für die Medizin.

[13]  Paul Over,et al.  TRECVID: evaluating the effectiveness of information retrieval tasks on digital video , 2004, MULTIMEDIA '04.

[14]  Henning Müller,et al.  A reference data set for the evaluation of medical image retrieval systems. , 2004, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

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

[16]  Christian Lovis,et al.  The Use of MedGIFT and EasyIR for ImageCLEF 2005 , 2005, CLEF.

[17]  Carol Peters,et al.  Multilingual Information Access for Text, Speech and Images, 5th Workshop of the Cross-Language Evaluation Forum, CLEF 2004, Bath, UK, September 15-17, 2004, Revised Selected Papers , 2005, CLEF.

[18]  Paul Clough,et al.  Overview of the 2005 cross-language image retrieval track (ImageCLEF) , 2005 .

[19]  Patrick Ruch,et al.  A Qualitative Task Analysis of Biomedical Image Use and Retrieval , 2005 .

[20]  Hermann Ney,et al.  FIRE in ImageCLEF 2005: Combining Content-based Image Retrieval with Textual Information Retrieval , 2005, CLEF.

[21]  Diana Maynard,et al.  Metrics for Evaluation of Ontology-based Information Extraction , 2006, EON@WWW.

[22]  Fredric C. Gey,et al.  Accessing Multilingual Information Repositories, 6th Workshop of the Cross-Language Evalution Forum, CLEF 2005, Vienna, Austria, 21-23 September, 2005, Revised Selected Papers , 2006, CLEF.

[23]  Hermann Ney,et al.  Sparse Patch-Histograms for Object Classification in Cluttered Images , 2006, DAGM-Symposium.

[24]  Hans Burkhardt,et al.  Image classification using cluster cooccurrence matrices of local relational features , 2006, MIR '06.

[25]  Thomas Deselaers,et al.  Overview of the ImageCLEF 2006 Photographic Retrieval and Object Annotation Tasks , 2006, CLEF.

[26]  Eugene Kim,et al.  Overview of the ImageCLEFmed 2006 Medical Retrieval and Annotation Tasks , 2006, CLEF.

[27]  Patrick Ruch,et al.  Model Formulation: Advancing Biomedical Image Retrieval: Development and Analysis of a Test Collection , 2006, J. Am. Medical Informatics Assoc..

[28]  Henning Müllera,et al.  Health care professionals ’ image use and search behaviour , 2006 .

[29]  Hermann Ney,et al.  The CLEF 2005 Automatic Medical Image Annotation Task , 2006, International Journal of Computer Vision.

[30]  Hans Burkhardt,et al.  Learning Taxonomies in Large Image Databases , 2007 .

[31]  Barbara Caputo,et al.  CLEF2007: Image Annotation Task: an SVM-based Cue Integration Approach , 2007, CLEF.

[32]  Heiko Schuldt,et al.  Speeding up IDM without Degradation of Retrieval Quality , 2008, CLEF.

[33]  Allan Hanbury,et al.  Overview of the ImageCLEFphoto 2007 Photographic Retrieval Task , 2008, CLEF.

[34]  Patrick Ruch,et al.  University and Hospitals of Geneva at ImageCLEF 2007 , 2007, CLEF.

[35]  Jorma Laaksonen,et al.  Overview of the ImageCLEF 2007 Object Retrieval Task , 2008, CLEF.