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Med Image Anal. 2016 Apr;29:47-64. doi: 10.1016/j.media.2015.12.006. Epub 2015 Dec 29.

Automatic detection of referral patients due to retinal pathologies through data mining.

Author information

1
Inserm, UMR 1101, SFR ScInBioS, F-29200 Brest, France. Electronic address: gwenole.quellec@inserm.fr.
2
Inserm, UMR 1101, SFR ScInBioS, F-29200 Brest, France; Univ Bretagne Occidentale, F-29200 Brest, France.
3
Service d'Ophtalmologie, Hôpital Lariboisière, APHP, F-75475 Paris, France.
4
Inserm, UMR 1101, SFR ScInBioS, F-29200 Brest, France; Univ Bretagne Occidentale, F-29200 Brest, France; Service d'Ophtalmologie, CHRU Brest, F-29200 Brest, France.
5
Inserm, UMR 1101, SFR ScInBioS, F-29200 Brest, France; Institut Mines-Telecom; Telecom Bretagne; UEB; Dpt ITI, F-29200 Brest, France.

Abstract

With the increased prevalence of retinal pathologies, automating the detection of these pathologies is becoming more and more relevant. In the past few years, many algorithms have been developed for the automated detection of a specific pathology, typically diabetic retinopathy, using eye fundus photography. No matter how good these algorithms are, we believe many clinicians would not use automatic detection tools focusing on a single pathology and ignoring any other pathology present in the patient's retinas. To solve this issue, an algorithm for characterizing the appearance of abnormal retinas, as well as the appearance of the normal ones, is presented. This algorithm does not focus on individual images: it considers examination records consisting of multiple photographs of each retina, together with contextual information about the patient. Specifically, it relies on data mining in order to learn diagnosis rules from characterizations of fundus examination records. The main novelty is that the content of examination records (images and context) is characterized at multiple levels of spatial and lexical granularity: 1) spatial flexibility is ensured by an adaptive decomposition of composite retinal images into a cascade of regions, 2) lexical granularity is ensured by an adaptive decomposition of the feature space into a cascade of visual words. This multigranular representation allows for great flexibility in automatically characterizing normality and abnormality: it is possible to generate diagnosis rules whose precision and generalization ability can be traded off depending on data availability. A variation on usual data mining algorithms, originally designed to mine static data, is proposed so that contextual and visual data at adaptive granularity levels can be mined. This framework was evaluated in e-ophtha, a dataset of 25,702 examination records from the OPHDIAT screening network, as well as in the publicly-available Messidor dataset. It was successfully applied to the detection of patients that should be referred to an ophthalmologist and also to the specific detection of several pathologies.

KEYWORDS:

Anomaly detection; Bag of Visual Words model; Data mining; Retinal pathologies

PMID:
26774796
DOI:
10.1016/j.media.2015.12.006
[Indexed for MEDLINE]

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