论文标题
绿色：用于分级糖尿病性视网膜病的图形残留重新排列网络
GREEN: a Graph REsidual rE-ranking Network for Grading Diabetic Retinopathy
论文作者
论文摘要
糖尿病性视网膜病（DR）的自动分级有助于患者和医生的医学诊断。现有研究将DR分级作为图像分类问题提出。由于DR的阶段/类别相互关联，因此无法通过单次旋转标签明确描述不同类别之间的关系，因为它是由不同结果的不同医生在经验上估计的。此类相关性限制了现有网络以实现有效的分类。在本文中，我们提出了一个图形残差重新排列网络（绿色），以将类依赖性在原始图像分类网络中引入。类依赖性先验由具有邻接矩阵的图形卷积网络表示。此前，通过重新排列分类来增强图像分类管道以残留的聚合方式导致。标准基准测试的实验表明，绿色对最先进的方法表现出色。
The automatic grading of diabetic retinopathy (DR) facilitates medical diagnosis for both patients and physicians. Existing researches formulate DR grading as an image classification problem. As the stages/categories of DR correlate with each other, the relationship between different classes cannot be explicitly described via a one-hot label because it is empirically estimated by different physicians with different outcomes. This class correlation limits existing networks to achieve effective classification. In this paper, we propose a Graph REsidual rE-ranking Network (GREEN) to introduce a class dependency prior into the original image classification network. The class dependency prior is represented by a graph convolutional network with an adjacency matrix. This prior augments image classification pipeline by re-ranking classification results in a residual aggregation manner. Experiments on the standard benchmarks have shown that GREEN performs favorably against state-of-the-art approaches.
