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A Semantic Fusion Approach Between Medical Images and Reports Using UMLS

A Semantic Fusion Approach Between Medical Images and Reports Using UMLS,10.1007/11880592_35,Daniel Racoceanu,Caroline Lacoste,Roxana Teodorescu,Nicol

A Semantic Fusion Approach Between Medical Images and Reports Using UMLS   (Citations: 5)
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One of the main challenges in content-based image retrieval still remains to bridge the gap between low-level features and seman- tic information. In this paper, we present our first results concerning a medical image retrieval approach using a semantic medical image and report indexing within a fusion framework, based on the Unified Medi- cal Language System (UMLS) metathesaurus. We propose a structured learning framework based on Support Vector Machines to facilitate mod- ular design and extract medical semantics from images. We developed two complementary visual indexing approaches within this framework: a global indexing to access image modality, and a local indexing to access semantic local features. Visual indexes and textual indexes - extracted from medical reports using MetaMap software application - constitute the input of the late fusion module. A weighted vectorial norm fusion algorithm allows the retrieval system to increase its meaningfulness, ef- ficiency and robustness. First results on the CLEF medical database are presented. The important perspectives of this approach in terms of se- mantic query expansion and data-mining are discussed.
Conference: Asia Information Retrieval Symposium - AIRS , pp. 460-475, 2006
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