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Content-Based Retrieval in Endomicroscopy: Toward an Efficient Smart Atlas for Clinical Diagnosis

Abstract : In this paper we present the first Content-Based Image Retrieval (CBIR) framework in the field of in vivo endomicroscopy, with applications ranging from training support to diagnosis support. We propose to adjust the standard Bag-of-Visual-Words method for the retrieval of endomicroscopic videos. Retrieval performance is evaluated both indirectly from a classification point-of-view, and directly with respect to a perceived similarity ground truth. The proposed method significantly outperforms, on two different endomicroscopy databases, several state-of-the-art methods in CBIR. With the aim of building a self-training simulator, we use retrieval results to estimate the interpretation difficulty experienced by the endoscopists. Finally, by incorporating clinical knowledge about perceived similarity and endomicroscopy semantics, we are able: 1) to learn an adequate visual similarity distance and 2) to build visual-word-based semantic signatures that extract, from low-level visual features, a higher-level clinical knowledge expressed in the endoscopist own language.
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Submitted on : Friday, May 3, 2013 - 2:01:38 PM
Last modification on : Saturday, June 25, 2022 - 11:09:56 PM
Long-term archiving on: : Sunday, August 4, 2013 - 2:45:13 AM


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  • HAL Id : hal-00813791, version 1



Barbara André, Tom Vercauteren, Nicholas Ayache. Content-Based Retrieval in Endomicroscopy: Toward an Efficient Smart Atlas for Clinical Diagnosis. Proceedings of the MICCAI Workshop - Medical Content-based Retrieval for Clinical Decision (MCBR-CDS'11), 2011, Toronto, Canada. pp.12-23. ⟨hal-00813791⟩



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