Cross-Image siamese graph convolutional network for Fine-Grained image retrieval in diabetic retinopathy

The categories of diabetic retinopathy (DR) are interrelated, and different ophthalmologists often give different results for the same fundus image. Automatic cross image retrieval of DR can provide an effective diagnostic solution for ophthalmologists and is of great significance in clinical practice. Cross-image (i.e. left and right fundus images for a patient) information is highly correlated and complementary and can be harnessed to improve various computer vision tasks such as image classification, object detection, image segmentation, and image retrieval. Previous studies did not explore the correlation between lesion areas in left and right fundus images of patients, limiting the effective diagnosis of DR. In this study, we proposed a cross-image siamese graph convolutional network(CIS-GCN) to retrieve fine-grained diabetic retinopathy fundus images. First, we constructed a global-specific structure to obtain the specific features of the left and right eyes. Then, we passed the specific features through the pathological localization network to obtain the location features of the lesion. Finally, a graph convolutional neural network was introduced to construct node sets for the left and right eyes, respectively, to represent relatively consistent regions in the fundus images of patients and learn their correlations. We tested our method using Diabetic Retinopathy Detection datasets and the results showed that our algorithm outperforms other state-of-the-art methods by 2.2 % similar to 3.7 % in image data retrieval.