Diabetic retinopathy (DR) and diabetic macular edema (DME) are the leading causes of permanent blindness in the working-age population. Automatic grading of DR and DME helps ophthalmologists design tailored treatments to patients, thus is of vital importance in the clinical practice. However, prior works either grade DR or DME, and ignore the correlation between DR and its complication, i.e., DME. Moreover, the location information, e.g., macula and soft hard exhaust annotations, are widely used as a prior for grading. Such annotations are costly to obtain, hence it is desirable to develop automatic grading methods with only image-level supervision. In this paper, we present a novel cross-disease attention network (CANet) to jointly grade DR and DME by exploring the internal relationship between the diseases with only image-level supervision. Our key contributions include the disease-specific attention module to selectively learn useful features for individual diseases, and the disease-dependent attention module to further capture the internal relationship between the two diseases. We integrate these two attention modules in a deep network to produce disease-specific and disease-dependent features, and to maximize the overall performance jointly for grading DR and DME. We evaluate our network on two public benchmark datasets, i.e., ISBI 2018 IDRiD challenge dataset and Messidor dataset. Our method achieves the best result on the ISBI 2018 IDRiD challenge dataset and outperforms other methods on the Messidor dataset. Our code is publicly available at https://github.com/xmengli999/CANet.

本研究介绍了一种跨病关注网络(CANet)，通过探索疾病之间的内部联系，只使用图像级监督方法联合评估DR和DME。通过疾病特异性关注模块和疾病依赖关注模块集成在一个深度网络中，以产生疾病特异性和疾病相关特征，并联合最大化DR和DME的整体表现。在ISBI 2018 IDRiD挑战数据集和Messidor数据集上评估了我们的网络，结果发现我们的方法在ISBI 2018 IDRiD挑战数据集上表现最佳，并且在Messidor数据集上优于其他方法。

CANet：面向联合糖尿病视网膜病变和糖尿病黄斑水肿评分的交叉疾病关注网络