The accurate diagnosis of fundus disease can effectively reduce the disease's further deterioration and provide targeted treatment plans for patients. Fundus image classification is a multi-label classification task due to one fundus image may contain one or more diseases. For multi-label classification of fundus images, we propose two new multi-label classification networks $--$ MCG-Net based on graph convolutional network and MCGS-Net based on graph convolutional network and self-supervised learning. Here, the graph convolutional network is used to capture the relevant information of the multi-label fundus images, and self-supervised learning is used to enhance the generalization ability of the network by learning more unannotated data. We use the ROC curve, Precision score, Recall score, Kappa score, F-1 score, and AUC value as the evaluation metrics and test on two datasets. Compared with other methods, our methods have better classification performance and generalization ability. Our methods can significantly improve classification performance and enhance the generalization ability of multi-label fundus image classification.

中文翻译：

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图卷积网络和自我监督学习对眼底图像进行多标签分类

眼底疾病的准确诊断可以有效减少疾病的进一步恶化，并为患者提供针对性的治疗计划。由于一个眼底图像可能包含一种或多种疾病，因此眼底图像分类是一项多标签分类任务。对于眼底图像的多标签分类，我们提出了两个新的多标签分类网络$-$基于图卷积网络的MCG-Net和基于图卷积网络和自我监督学习的MCGS-Net。在这里，图卷积网络用于捕获多标签眼底图像的相关信息，而自我监督学习则用于通过学习更多未注释的数据来增强网络的泛化能力。我们使用ROC曲线，精度得分，召回得​​分，Kappa得分，F-1得分和AUC值作为评估指标，并在两个数据集上进行测试。与其他方法相比，我们的方法具有更好的分类性能和泛化能力。我们的方法可以显着提高分类性能，增强多标签眼底图像分类的泛化能力。