Abstract
Oregon Health & Science University participated in both the medical retrieval and medical annotation tasks of ImageCLEF 2006. Our efforts in the retrieval task focused on manual modification of query statements and fusion of results from textual and visual retrieval techniques. Our results showed that manual modification of queries does improve retrieval performance, while data fusion of textual and visual techniques improves precision but lowers recall. However, since image retrieval may be a precision-oriented task, these data fusion techniques could be of value for many users. In the annotation task, we assessed a variety of learning techniques and obtained classification accuracy of up to 74% with test data.
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Hersh, W., Kalpathy-Cramer, J., Jensen, J. (2007). Medical Image Retrieval and Automated Annotation: OHSU at ImageCLEF 2006. In: Peters, C., et al. Evaluation of Multilingual and Multi-modal Information Retrieval. CLEF 2006. Lecture Notes in Computer Science, vol 4730. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74999-8_81
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DOI: https://doi.org/10.1007/978-3-540-74999-8_81
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