Medical case retrieval from a committee of decision trees

Authors:
Gwénolé Quellec;Mathieu Lamard;Lynda Bekri;Guy Cazuguel;Christian Roux;Béatrice Cochener
Affiliations:
INSTITUT TELECOM/TELECOM Bretagne, Dpt ITI. Brest, France, and Institut National de la Santé et de la Recherche Médicale, Brest, France;University of Bretagne Occidentale, Brest, France, and Institut National de la Santé et de la Recherche Médicale, Brest, France;Institut National de la Santé et de la Recherche Médicale, Brest, France and CHU Brest, Brest, France;INSTITUT TELECOM/TELECOM Bretagne, Dpt ITI. Brest, France, and Institut National de la Santé et de la Recherche Médicale, Brest, France;INSTITUT TELECOM/TELECOM Bretagne, Dpt ITI. Brest, France, and Institut National de la Santé et de la Recherche Médicale, Brest, France;Centre Hospitalier Universitaire Brest, Brest, France and University of Bretagne Occidentale, Brest, France, and Institut National de la Santé et de la Recherche Médicale, Brest, France
Venue:
IEEE Transactions on Information Technology in Biomedicine
Year:
2010

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Abstract

A novel content-based information retrieval framework, designed to cover several medical applications, is presented in this paper. The presented framework allows the retrieval of possibly incomplete medical cases consisting of several images together with semantic information. It relies on a committee of decision trees, decision support tools well suited to process this type of information. In our proposed framework, images are characterized by their digital content. It was applied to two heterogeneous medical datasets for computer-aided diagnoses: a diabetic retinopathy follow-up dataset (DRD) and a mammography-screening dataset (DDSM). Measure of precision among the top five retrieved results of 0.788 ± 0.137 and 0.869 ± 0.161 was obtained on DRD and DDSM, respectively. On DRD, for instance, it increases by half the retrieval of single images.