| titre : |
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Multimodal medical case retrieval using Bayesian networks and the Dezert-Smarandache theory |
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| auteur(s) : |
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Gwénolé Quellec1, 2, Mathieu Lamard ( ) 1, Lynda Bekri1, 3, Guy Cazuguel1, 2, Christian Roux1, 2, Béatrice Cochener1, 3 |
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| laboratoire : |
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| résumé : |
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Most medical images are now digitized and stored with semantic information, leading to medical case databases. They may be used for aid to diagnosis, by retrieving similar cases to those in examination. But the information are often incomplete, uncertain and sometimes conflicting, so difficult to use. In this paper, we present a Case Based Reasoning (CBR) system for medical case retrieval, derived from the Dezert-Smarandache theory, which is well suited to handle those problems. We introduce a case retrieval specific frame of discernment theta, which associates each element of theta with a case in the database; we take advantage of the flexibility offered by the DSmT's hybrid models to finely model the database. The system is designed so that heterogeneous sources of information can be integrated in the system: in particular images, indexed by their digital content, and symbolic information. The method is evaluated on two classified databases: one for diabetic retinopathy follow-up (DRD) and one for screening mammography (DDSM). On these databases, results are promising: the retrieval precision at five reaches 81.8% on DRD and 84.8% on DDSM. |
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| domaine : |
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Sciences du Vivant/Ingénierie biomédicale
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langue du texte
intégral : |
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Anglais |
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| ISSN : |
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1557-170X |
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| type de publication : |
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Articles dans des revues avec comité de lecture |
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| DOI : |
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10.1109/ISBI.2008.4540978 |
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| journal : |
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5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro |
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| Audience : |
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internationale |
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| date de publication : |
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05/2008 |
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| numéro : |
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14-17 |
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| page, identifiant, ... : |
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245-248 |
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| mots-clés auteur : |
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Bayesian networks – Case based reasoning – Dezert-Smarandache theory – Diabetic Retinopathy – Image indexing |
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