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Review
. 2015 Jan:39:55-61.
doi: 10.1016/j.compmedimag.2014.03.004. Epub 2014 Mar 27.

Evaluating performance of biomedical image retrieval systems--an overview of the medical image retrieval task at ImageCLEF 2004-2013

Jayashree Kalpathy-Cramer  1 , Alba García Seco de Herrera  2 , Dina Demner-Fushman  3 , Sameer Antani  3 , Steven Bedrick  4 , Henning Müller  2
Affiliations
Review

Evaluating performance of biomedical image retrieval systems--an overview of the medical image retrieval task at ImageCLEF 2004-2013

Jayashree Kalpathy-Cramer et al. Comput Med Imaging Graph. 2015 Jan.

Abstract

Medical image retrieval and classification have been extremely active research topics over the past 15 years. Within the ImageCLEF benchmark in medical image retrieval and classification, a standard test bed was created that allows researchers to compare their approaches and ideas on increasingly large and varied data sets including generated ground truth. This article describes the lessons learned in ten evaluation campaigns. A detailed analysis of the data also highlights the value of the resources created.

Keywords: Biomedical literature; Content-based retrieval; Image retrieval; Multimodal medical retrieval; Text-based image retrieval.

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Figures

Figure 1
Figure 1
Number of images in the collection
Figure 2
Figure 2
Number of groups submitting runs for the medical task
Figure 3
Figure 3
Total runs submitted for the medical task
See this image and copyright information in PMC

References

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    1. Caputo B, Müller H, Thomee B, Villegas M, Paredes R, Zellhofer D, Goeau H, Joly A, Bonnet P, Martinez Gomez J, Garcia Varea I, Cazorla C. Working Notes of CLEF 2013 (Cross Language Evaluation Forum) 2013. ImageCLEF 2013: the vision, the data and the open challenges.
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MeSH terms