This paper describes a methodology for diabetic retinopathy detection from eye fundus images using a generalization of the bag-of-visual-words (BoVW) method. We formulate the BoVW as two neural networks that can be trained jointly. Unlike the BoVW, our model is able to learn how to perform feature extraction, feature encoding, and classification guided by the classification error. The model achieves 0.97 area under the curve (AUC) on the DR2 dataset while the standard BoVW approach achieves 0.94 AUC. Also, it performs at the same level of the state-of-the-art on the Messidor dataset with 0.90 AUC.

中文翻译：

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从眼底图像中检测出卷积词的糖尿病性视网膜病变

本文描述了一种使用眼袋（BoVW）方法的推广从眼底图像检测糖尿病性视网膜病变的方法。我们将BoVW公式化为可以共同训练的两个神经网络。与BoVW不同，我们的模型能够学习如何在分类错误的指导下进行特征提取，特征编码和分类。该模型在DR2数据集上的曲线下面积（AUC）为0.97，而标准BoVW方法则为0.94 AUC。而且，它在Messidor数据集上具有0.90 AUC的最新水平。