GFF-Net: Graph-based feature fusion network for diagnosing plus disease in retinopathy of prematurity

Abstract

Retinopathy of prematurity (ROP) is a retinal proliferative disorder, and it is the primary cause of childhood blindness. Accurate and convenient automatic diagnostic tools are required to assist ophthalmologists in diagnosing ROP. Existing methods only extract information from fundus image captured from posterior angle, while images captured from other angles are ignored, which limits the performance of the algorithm. In this paper, we propose a graph-based feature fusion network (GFF-Net) that can jointly analyze multiple images and make full use of the relevant information between these images to diagnose the plus disease in ROP. The convolutional features of different fundus images are connected into a graph, where the edges of the graph model the correlation between these images. A graph-based feature fusion module is proposed to aggregate features from the constructed feature graph and produce the final prediction. We compared the proposed GFF-Net with state-of-the-art methods on a clinical dataset and a low-quality “attack dataset". The GFF-Net achieved superior performance compared to other methods on both datasets. The results show that the proposed GFF-Net could be more effective than existing methods in clinical practice.