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Simultaneous Diagnosis of Severity and Features of Diabetic Retinopathy in Fundus Photography Using Deep Learning.
IEEE Journal of Biomedical and Health Informatics ( IF 6.7 ) Pub Date : 2020-07-28 , DOI: 10.1109/jbhi.2020.3012547
Juan Wang , Yujing Bai , Bin Xia

Deep learning methods for diabetic retinopathy (DR) diagnosis are usually criticized as being lack of interpretability in the diagnostic result, thus limiting their application in clinic. Simultaneous prediction of DR related features during the DR severity diagnosis is able to resolve this issue by providing supporting evidence (i.e. DR related features) for the diagnostic result (i.e. DR severity). In this study, we propose a hierarchical multi-task deep learning framework for simultaneous diagnosis of DR severity and DR related features in fundus images. A hierarchical structure is introduced to incorporate the casual relationship between DR related features and DR severity levels. In the experiments, the proposed approach was evaluated on two independent testing sets using quadratic weighted Cohen's kappa coefficient, receiver operating characteristic analysis, and precision-recall analysis. A grader study was also conducted to compare the performance of the proposed approach with those of general ophthalmologists with different levels of experience. The results demonstrate that the proposed approach could improve the performance for both DR severity diagnosis and DR related feature detection when comparing with the traditional deep learning-based methods. It achieves performance close to general ophthalmologists with five years of experience when diagnosing DR severity levels, and general ophthalmologists with ten years of experience for referable DR detection.

中文翻译:

使用深度学习技术同时诊断眼底照相术中糖尿病性视网膜病变的严重程度和特征。

糖尿病性视网膜病变(DR)诊断的深度学习方法通​​常被批评为诊断结果缺乏可解释性,因此限制了其在临床中的应用。通过为诊断结果(即DR严重性)提供支持证据(即与DR相关的功能),可以在DR严重性诊断期间同时预测DR的相关特征。在这项研究中,我们提出了一个分层的多任务深度学习框架,用于同时诊断眼底图像中的DR严重性和DR相关特征。引入了层次结构,以合并DR相关功能和DR严重性级别之间的随意关系。在实验中,使用二次加权Cohenκ系数在两个独立的测试集上评估了所提出的方法,接收器工作特性分析和精确召回分析。还进行了一个分级研究,以比较该建议方法与具有不同经验水平的普通眼科医生的效果。结果表明,与传统的基于深度学习的方法相比,该方法可以提高DR严重性诊断和DR相关特征检测的性能。在诊断DR严重性水平方面,它的性能接近具有5年经验的普通眼科医生和具有10年可参考DR检测经验的普通眼科医生。还进行了一个分级研究,以比较该建议方法与具有不同经验水平的普通眼科医生的效果。结果表明,与传统的基于深度学习的方法相比,该方法可以提高DR严重性诊断和DR相关特征检测的性能。在诊断DR严重性水平方面,它的性能接近具有5年经验的普通眼科医生和具有10年可参考DR检测经验的普通眼科医生。还进行了一个分级研究,以比较该建议方法与具有不同经验水平的普通眼科医生的效果。结果表明,与传统的基于深度学习的方法相比,该方法可以提高DR严重性诊断和DR相关特征检测的性能。在诊断DR严重性水平方面,它的性能接近具有5年经验的普通眼科医生和具有10年可参考DR检测经验的普通眼科医生。
更新日期:2020-07-28
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