Abstract
To facilitate the automatic indexing and retrieval of large medical image databases, images and associated texts are indexed using concepts from the Unified Medical Language System (UMLS) meta-thesaurus. We propose a structured learning framework for learning medical semantics from images. Two complementary global and local visual indexing approaches are presented. Two fusion approaches are also used to improve textual retrieval using the UMLS-based image indexing: a simple post-query fusion and a visual modality filtering to remove visually aberrant images according to the query modality concepts. Using the ImageCLEFmed database, we demonstrate that our framework is superior when compared to the automatic runs evaluated in 2005 on the same Medical Image Retrieval task.
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Lim, JH., Chevallet, JP., Le, D.T.H., Goh, H. (2008). Bi-modal Conceptual Indexing for Medical Image Retrieval. In: Satoh, S., Nack, F., Etoh, M. (eds) Advances in Multimedia Modeling. MMM 2008. Lecture Notes in Computer Science, vol 4903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77409-9_43
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DOI: https://doi.org/10.1007/978-3-540-77409-9_43
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