Diabetic Retinopathy (DR) is a leading cause of vision loss globally. Yet despite its prevalence, the majority of affected people lack access to the specialized ophthalmologists and equipment required for assessing their condition. This can lead to delays in the start of treatment, thereby lowering their chances for a successful outcome. Machine learning systems that automatically detect the disease in eye fundus images have been proposed as a means of facilitating access to DR severity estimates for patients in remote regions or even for complementing the human expert's diagnosis. In this paper, we propose a machine learning system for the detection of referable DR in fundus images that is based on the paradigm of multiple-instance learning. By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy. Moreover, it can highlight potential image regions where DR manifests through its characteristic lesions. We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance, while also producing interpretable visualizations of its predictions.
翻译:在全球范围,糖尿病视网膜病(DR)是导致视力丧失的一个主要原因。然而,尽管其流行,大多数受影响者仍无法获得评估其病情所需的专业眼科医生和设备。这可能导致治疗的开始出现延误,从而降低其成功的机会。在眼球上自动检测该疾病的机器学习系统被提议作为一种手段,便利偏远地区患者获得DR严重程度估计,甚至补充人类专家的诊断。我们在本文件中提议建立一个机器学习系统,用以检测基于多功能学习模式的基金图象中的可参考的DR。通过从图像补丁提取当地信息并通过关注机制有效地将其组合起来,我们的系统能够实现高分类准确性。此外,该系统还可以突出DR通过其特征性缺陷显示的潜在图像区域。我们评估了我们公开提供的视网图像数据集的方法,其中显示其近于最先进的性功能,同时制作其预测的可解释可视化的可视化数据。