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Medical Image Annotation in ImageCLEF 2008

  • Thomas Deselaers
  • Thomas M. Deserno
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5706)

Abstract

The ImageCLEF 2008 medical image annotation task is designed to assess the quality of content-based image retrieval and image classification by means of global signatures. In contrast to the previous years, the 2008 task was designed such that the hierarchy of reference IRMA code classifications is essential for good performance. In total, 12076 images were used, and 24 runs of 6 groups were submitted. Multi-class classification schemes for support vector machines outperformed the other methods. A scoring scheme was defined to penalise wrong classification in early code positions over those in later branches of the code hierarchy, and to penalise false category association over the assignment of a “not known” code. The obtained scores rage from 74.92 over 182.77 to 313.01 for best, baseline and worst results, respectively.

Keywords

Support Vector Machine Image Retrieval Training Image Retrieval Task Code Position 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Thomas Deselaers
    • 1
  • Thomas M. Deserno
    • 2
  1. 1.Computer Science DepartmentRWTH Aachen UniversityAachenGermany
  2. 2.Dept. of Medical InformaticsRWTH Aachen UniversityAachenGermany

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