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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.
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Contributor : Gwénolé Quellec <>
Submitted on : Thursday, November 22, 2007 - 3:12:22 PM
Last modification on : Wednesday, June 24, 2020 - 4:18:37 PM
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Gwénolé Quellec, Mathieu Lamard, Lynda Bekri, Guy Cazuguel, Béatrice Cochener, et al.. Multimedia medical case retrieval using decision trees.. Conference proceedings : .. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Institute of Electrical and Electronics Engineers (IEEE), 2007, 1, pp.4536-9. ⟨10.1109/IEMBS.2007.4353348⟩. ⟨inserm-00189853⟩



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