  Diabetic retinopathy (DR) grading from fundus images has attracted increasing
interest in both academic and industrial communities. Most convolutional neural
network (CNN) based algorithms treat DR grading as a classification task via
image-level annotations. However, these algorithms have not fully explored the
valuable information in the DR-related lesions. In this paper, we present a
robust framework, which collaboratively utilizes patch-level and image-level
annotations, for DR severity grading. By an end-to-end optimization, this
framework can bi-directionally exchange the fine-grained lesion and image-level
grade information. As a result, it exploits more discriminative features for DR
grading. The proposed framework shows better performance than the recent
state-of-the-art algorithms and three clinical ophthalmologists with over nine
years of experience. By testing on datasets of different distributions (such as
label and camera), we prove that our algorithm is robust when facing image
quality and distribution variations that commonly exist in real-world practice.
We inspect the proposed framework through extensive ablation studies to
indicate the effectiveness and necessity of each motivation. The code and some
valuable annotations are now publicly available.
