Abstract
We promote the use of explicit medical knowledge to solve retrieval of information both visual and textual. For text, this knowledge is a set of concepts from a Meta-thesaurus, the Unified Medical Language System (UMLS). For images, this knowledge is a set of semantic features that are learned from examples using SVM within a structured learning framework. Image and text index are represented in the same way: a vector of concepts. The use of concepts allows the expression of a common index form: an inter-media index, offering the opportunity of homogeneous indexing/querying time fusion techniques. Top results obtained with concept based approaches show the potential of conceptual indexing. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Lacoste, C., Chevallet, J. P., Lim, J. H., Le, D. T. H., Xiong, W., Racoceanu, D., … Vuillenemot, N. (2007). Inter-media concept-based medical image indexing and retrieval with UMLS at IPAL. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4730 LNCS, pp. 694–701). Springer Verlag. https://doi.org/10.1007/978-3-540-74999-8_86
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