title: Multimedia medical case retrieval using decision trees.
abstract: In this paper, we present a Case Based Reasoning (CBR) system for the retrieval of medical cases made up of a series of images with contextual information (such as the patient age, sex and medical history). Indeed, medical experts generally need varied sources of information (which might be incomplete) to diagnose a pathology. Consequently, we derive a retrieval framework from decision trees, which are well suited to process heterogeneous and incomplete information. To be integrated in the system, images are indexed by their digital content. The method is evaluated on a classified diabetic retinopathy database. On this database, results are promising: the retrieval sensitivity reaches 79.5% for a window of 5 cases, which is almost twice as good as the retrieval of single images alone. As a comparison, the retrieval sensitivity is 52.3% for a standard multimodal case retrieval using a linear combination of heterogeneous distances.
authors.affiliation: ENST Bretagne, GET-ENST, Brest, F-29200 France. gwenole.quellec@enst-bretagne.fr
authors.forename: Gwénolé
authors.initials: G
authors.lastname: Quellec
authors.forename: Mathieu
authors.initials: M
authors.lastname: Lamard
authors.forename: Lynda
authors.initials: L
authors.lastname: Bekri
authors.forename: Guy
authors.initials: G
authors.lastname: Cazuguel
authors.forename: Béatrice
authors.initials: B
authors.lastname: Cochener
authors.forename: Christian
authors.initials: C
authors.lastname: Roux
mesh_terms: D016208:Databases, Factual; D003658:Decision Making, Computer-Assisted; D003930:Diabetic Retinopathy / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016347:Medical Records Systems, Computerized