Multimedia data mining for automatic diabetic retinopathy screening

Gwénolé Quellec 1 Mathieu Lamard 1 Béatrice Cochener 2, 1 Etienne Decencière 3 Bruno Lay 4 Agnès Chabouis 5 Christian Roux 6 Guy Cazuguel 7
6 LaTIM
LaTIM - Laboratoire de Traitement de l'Information Medicale, DS - Direction Scientifique
7 LaTIM
LaTIM - Laboratoire de Traitement de l'Information Medicale, ITI - Département Image et Traitement Information
Abstract : — This paper presents TeleOphta, an automatic sys-tem for screening diabetic retinopathy in teleophthalmology networks. Its goal is to reduce the burden on ophthalmologists by automatically detecting non referable examination records, i.e. examination records presenting no image quality problems and no pathological signs related to diabetic retinopathy or any other retinal pathology. TeleOphta is an attempt to put into practice years of algorithmic developments from our groups. It combines image quality metrics, specific lesion detectors and a generic pathological pattern miner to process the visual content of eye fundus photographs. This visual information is further combined with contextual data in order to compute an abnormality risk for each examination record. The TeleOphta system was trained and tested on a large dataset of 25,702 examination records from the OPHDIAT screening network in Paris. It was able to automatically detect 68% of the non referable examination records while achieving the same sensitivity as a second ophthalmologist. This suggests that it could safely reduce the burden on ophthalmologists by 56%.
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Communication dans un congrès
Engineering in Medicine and Biology Society (EMBC), Jul 2013, Osaka, Japan. pp.7144 - 7147, 2013, <10.1109/EMBC.2013.6611205>
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https://hal-mines-paristech.archives-ouvertes.fr/hal-01082869
Contributeur : Etienne Decencière <>
Soumis le : vendredi 14 novembre 2014 - 14:58:24
Dernière modification le : mardi 12 septembre 2017 - 11:41:11
Document(s) archivé(s) le : dimanche 15 février 2015 - 11:05:11

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Gwénolé Quellec, Mathieu Lamard, Béatrice Cochener, Etienne Decencière, Bruno Lay, et al.. Multimedia data mining for automatic diabetic retinopathy screening. Engineering in Medicine and Biology Society (EMBC), Jul 2013, Osaka, Japan. pp.7144 - 7147, 2013, <10.1109/EMBC.2013.6611205>. <hal-01082869>

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