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

Medical Image Analysis
Gwénolé QuellecGuy Cazuguel

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 r...Continue Reading

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Citations

Nov 13, 2019·The International Journal of Medical Robotics + Computer Assisted Surgery : MRCAS·Constantinos Loukas, Nicholas P Sgouros
Jul 12, 2019·Interdisciplinary Sciences, Computational Life Sciences·Annabella AstorinoEugenio Vocaturo
May 14, 2020·Applied Optics·J E Gómez-CorreaS Chávez-Cerda

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