Abstract
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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Müller, H., Kalpathy-Cramer, J. (2010). The ImageCLEF Medical Retrieval Task at ICPR 2010 — Information Fusion to Combine Visual and Textual Information. In: Ünay, D., Çataltepe, Z., Aksoy, S. (eds) Recognizing Patterns in Signals, Speech, Images and Videos. ICPR 2010. Lecture Notes in Computer Science, vol 6388. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17711-8_11
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DOI: https://doi.org/10.1007/978-3-642-17711-8_11
Publisher Name: Springer, Berlin, Heidelberg
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