Medical case retrieval from a committee of decision trees.
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 + or - 0.137 and 0.869 + or - 0.161 was obtained on DRD and DDSM, respectively. On DRD, for instance, it increases by half the retrieval of single images.
Quellec
Gwénolé
G
INSTITUT TELECOM/TELECOM Bretagne, Dpt ITI, Brest, F-29200 France. gwenole.quellec@telecom-bretagne.eu
Lamard
Mathieu
M
Bekri
Lynda
L
Cazuguel
Guy
G
Roux
Christian
C
Cochener
Béatrice
B
D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D020000:Decision Support Systems, Clinical; D003663:Decision Trees; D003930:Diabetic Retinopathy / Q000175:diagnosis; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005654:Fundus Oculi; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016247:Information Storage and Retrieval / Q000379:methods*; D008327:Mammography; D012815:Signal Processing, Computer-Assisted