TY - GEN
T1 - The ImageCLEF medical retrieval task at ICPR 2010 - Information fusion to combine visual and textual information
AU - Müller, Henning
AU - Kalpathy-Cramer, Jayashree
N1 - Funding Information:
We would like to acknowledge the support of the National Library of Medicine grant 1K99LM009889 and of the BeMeVIS project of the HES–SO.
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 - 10.1007/978-3-642-17711-8_11
DO - 10.1007/978-3-642-17711-8_11
M3 - Conference contribution
AN - SCOPUS:78650833268
SN - 3642177107
SN - 9783642177101
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 99
EP - 108
BT - Recognizing Patterns in Signals, Speech, Images, and Videos - ICPR 2010 Contests, Contest Reports
T2 - 20th International Conference on Pattern Recognition, ICPR 2010
Y2 - 23 August 2010 through 26 August 2010
ER -