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Comput Med Imaging Graph. 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.

Author information

1
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. Electronic address: kalpathy@nmr.mgh.harvard.edu.
2
University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland.
3
National Library of Medicine/National Institutes of Health (NLM/NIH), Bethesda, MD, USA.
4
Oregon Health & Science University, Portland, OR, USA.

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

PMID:
24746250
PMCID:
PMC4177510
DOI:
10.1016/j.compmedimag.2014.03.004
[Indexed for MEDLINE]
Free PMC Article

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