Diabetic Retinopathy Grading with Weakly-supervised Lesion Priors
Junlin Hou (Fudan University); Fan Xiao (Fudan University); Jilan Xu (Fudan University); Rui Feng (Fudan University); Yuejie Zhang (Fudan University); Haidong Zou (Shanghai Eye Diseases Prevention and Treatment Center); Lina Lu (Shanghai Eye Diseases Prevention and Treatment Center); Wenwen Xue (Shanghai Eye Diseases Prevention and Treatment Center)
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Explicit information of lesions can provide visual instructions for diabetic retinopathy (DR) grading on fundus images. However, pixel-level lesion annotations are extremely difficult and time-consuming to acquire. In this work, we propose a novel weakly-supervised lesion-aware network for DR grading, which enhances the discriminative features with lesion priors by only image-level supervision. Specifically, we design a lesion attention module that generates lesion activation maps by introducing an auxiliary task of binary DR identification. Lesion activation maps are utilized to assist the network to focus on the most relevant regions for boosting DR grading performance. Besides, we particularly devise an adaptive joint loss to balance the DR identification and DR grading tasks dynamically. Extensive results on the public DR dataset demonstrate the superiority and generality of our proposed lesion-aware network. The interpretability of generated lesion activation maps is also verified by the comparison with ground truth segmentation masks.