@article{KALPATHYCRAMER201555,
 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.},
 author = {Jayashree Kalpathy-Cramer and Alba Garcı́a Seco de Herrera and Dina Demner-Fushman and Sameer Antani and Steven Bedrick and Henning Müller},
 bdsk-url-1 = {http://www.sciencedirect.com/science/article/pii/S0895611114000366},
 bdsk-url-2 = {https://doi.org/10.1016/j.compmedimag.2014.03.004},
 date-added = {2018-07-31 01:03:59 +0000},
 date-modified = {2018-07-31 01:03:59 +0000},
 doi = {https://doi.org/10.1016/j.compmedimag.2014.03.004},
 issn = {0895-6111},
 journal = {Computerized Medical Imaging and Graphics},
 keywords = {Multimodal medical retrieval, Image retrieval, Biomedical literature, Content-based retrieval, Text-based image retrieval},
 note = {Medical visual information analysis and retrieval},
 pages = {55 - 61},
 title = {Evaluating performance of biomedical image retrieval systems---An overview of the medical image retrieval task at ImageCLEF 2004--2013},
 url = {http://www.sciencedirect.com/science/article/pii/S0895611114000366},
 volume = {39},
 year = {2015}
}