Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 5180000, China.
School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510000, China.
Sci Data. 2020 Nov 20;7(1):409. doi: 10.1038/s41597-020-00755-0.
Automated detection of exudates from fundus images plays an important role in diabetic retinopathy (DR) screening and evaluation, for which supervised or semi-supervised learning methods are typically preferred. However, a potential limitation of supervised and semi-supervised learning based detection algorithms is that they depend substantially on the sample size of training data and the quality of annotations, which is the fundamental motivation of this work. In this study, we construct a dataset containing 1219 fundus images (from DR patients and healthy controls) with annotations of exudate lesions. In addition to exudate annotations, we also provide four additional labels for each image: left-versus-right eye label, DR grade (severity scale) from three different grading protocols, the bounding box of the optic disc (OD), and fovea location. This dataset provides a great opportunity to analyze the accuracy and reliability of different exudate detection, OD detection, fovea localization, and DR classification algorithms. Moreover, it will facilitate the development of such algorithms in the realm of supervised and semi-supervised learning.
眼底图像中渗出物的自动检测在糖尿病性视网膜病变（DR）筛查和评估中起着重要作用，为此通常首选监督式或半监督式学习方法。然而，基于监督式和半监督式学习的检测算法的一个潜在局限性是，它们在很大程度上依赖于训练数据的样本量和注释的质量，这正是这项工作的基本动机。在这项研究中，我们构建了一个包含 1219 张眼底图像（来自 DR 患者和健康对照者）的数据集，这些图像带有渗出病变的注释。除了渗出物的注释，我们还为每张图像提供了四个附加标签：左眼与右眼标签、来自三个不同分级方案的 DR 等级（严重程度量表）、视盘（OD）的边界框和黄斑位置。该数据集为分析不同渗出物检测、OD 检测、黄斑定位和 DR 分类算法的准确性和可靠性提供了极好的机会。此外，它将促进监督式和半监督式学习领域中此类算法的发展。
