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

机译：通过数据挖掘自动检测由于视网膜病变引起的转诊患者

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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. (C) 2016 Elsevier B.V. All rights reserved.
机译：随着视网膜病理学的流行，自动化检测这些病理学变得越来越重要。在过去的几年中，已经开发了许多算法，可以使用眼底照相技术自动检测特定的病理，通常是糖尿病性视网膜病变。不管这些算法有多好，我们相信许多临床医生不会使用自动检测工具来关注单一病理学，而忽略患者视网膜中存在的任何其他病理学。为了解决这个问题，提出了一种用于表征异常视网膜的外观以及正常视网膜的外观的算法。该算法不专注于单个图像：它考虑了由每个视网膜的多张照片以及有关患者的上下文信息组成的检查记录。具体而言，它依靠数据挖掘以从眼底检查记录的特征中学习诊断规则。主要的新颖之处在于检查记录（图像和上下文）的内容在多个级别的空间和词汇粒度上进行了表征：1）通过将合成视网膜图像自适应分解为一系列区域来确保空间灵活性; 2）词汇粒度通过将特征空间自适应分解为一系列的视觉单词来确保。这种多粒度表示形式在自动表征正常性和异常性方面提供了极大的灵活性：可以生成诊断规则，其诊断精度和泛化能力可以根据数据可用性进行权衡。提出了最初用于挖掘静态数据的常规数据挖掘算法的一种变体，以便可以在自适应粒度级别上挖掘上下文和可视数据。该框架在e-ophtha中进行了评估，e-ophtha是OPHDIAT筛查网络中25,702条检查记录的数据集，也是可公开获得的Messidor数据集。它已成功地应用于应转诊给眼科医生的患者的检测以及多种病理的特异性检测。 （C）2016 Elsevier B.V.保留所有权利。

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