TY - GEN
T1 - The ImageCLEF medical retrieval task at ICPR 2010 information fusion
AU - Müller, Henning
AU - Kalpathy-Cramer, Jayashree
PY - 2010
Y1 - 2010
N2 - An increasing number of clinicians, researchers, educators and patients routinely search for medical information on the Internet as well as in image archives. However, image retrieval is far less understood and developed than text-based search. The ImageCLEF medical image retrieval task is an international benchmark that enables researchers to assess and compare techniques for medical image retrieval using standard test collections. Although text retrieval is mature and well researched, it is limited by the quality and availability of the annotations associated with the images. Advances in computer vision have led to methods for using the image itself as search entity. However, the success of purely content-based techniques has been limited and these systems have not had much clinical success. On the other hand a combination of text- and content-based retrieval can achieve improved retrieval performance if combined effectively. Combining visual and textual runs is not trivial based on experience in ImageCLEF. The goal of the fusion challenge at ICPR is to encourage participants to combine visual and textual results to improve search performance. Participants were provided textual and visual runs, as well as the results of the manual judgments from ImageCLEFmed 2008 as training data. The goal was to combine textual and visual runs from 2009. In this paper, we present the results from this ICPR contest.
AB - An increasing number of clinicians, researchers, educators and patients routinely search for medical information on the Internet as well as in image archives. However, image retrieval is far less understood and developed than text-based search. The ImageCLEF medical image retrieval task is an international benchmark that enables researchers to assess and compare techniques for medical image retrieval using standard test collections. Although text retrieval is mature and well researched, it is limited by the quality and availability of the annotations associated with the images. Advances in computer vision have led to methods for using the image itself as search entity. However, the success of purely content-based techniques has been limited and these systems have not had much clinical success. On the other hand a combination of text- and content-based retrieval can achieve improved retrieval performance if combined effectively. Combining visual and textual runs is not trivial based on experience in ImageCLEF. The goal of the fusion challenge at ICPR is to encourage participants to combine visual and textual results to improve search performance. Participants were provided textual and visual runs, as well as the results of the manual judgments from ImageCLEFmed 2008 as training data. The goal was to combine textual and visual runs from 2009. In this paper, we present the results from this ICPR contest.
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U2 - 10.1109/ICPR.2010.803
DO - 10.1109/ICPR.2010.803
M3 - Conference contribution
AN - SCOPUS:78149474966
SN - 9780769541099
T3 - Proceedings - International Conference on Pattern Recognition
SP - 3284
EP - 3287
BT - Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
T2 - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Y2 - 23 August 2010 through 26 August 2010
ER -