Overview of the ImageCLEFmed 2008 medical image retrieval task

  • Authors:

  • Henning Müller;Jayashree Kalpathy-Cramer;Charles E. Kahn, Jr.;William Hatt;Steven Bedrick;William Hersh

  • Affiliations:

  • Medical Informatics, University Hospitals and University of Geneva, Switzerland and University of Applied Sciences Western Switzerland, Sierre, Switzerland;Oregon Health and Science University, Portland, OR;Department of Radiology, Medical College of Wisconsin, Milwaukee, WI;Oregon Health and Science University, Portland, OR;Oregon Health and Science University, Portland, OR;Oregon Health and Science University, Portland, OR

  • Venue:
  • CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
  • Year:
  • 2008

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Abstract

The medical image retrieval task of ImageCLEF is in its fifth year and participation continues to increase to a total of 37 registered research groups. About half the registered groups finally submit results. Main change in 2008 was the use of a new databases containing images of the medical scientific literature (articles from the Journals Radiology and Radiographics). Besides the images, the figure captions and the part of the caption referring to a particular sub-figure were supplied as well as access to the full text articles in html. All texts were in English and the topics were supplied in German, French, and English. 30 topics were made available, ten of each of the categories visual, mixed, semantic. Most groups concentrated on fully automatic retrieval. Only three groups submitted a total of six manual or interactive runs not showing an increase of performance over automatic approaches. In previous years, multi-modal combinations were the most frequent submissions but in 2008 text only runs were clearly higher. Only very few fully visual runs were submitted and non of the fully visual runs had an extremely good performance. Part of these tendencies might be due to semantic topics and the extremely well annotated database. Best results regarding MAP were similar for textual and multi-modal approaches whereas early precision was better for some multi-modal approaches.