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

IF 3.4 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Applied Intelligence Pub Date : 2023-08-07 DOI:10.1007/s10489-023-04766-3
Kaide Huang, Wentao Dong, Jie Li, Yuanyuan Chen, Jie Zhong, Zhang Yi
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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.

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GFF-Net:用于诊断早产儿视网膜病变的基于图的特征融合网络
早产儿视网膜病变(ROP)是一种视网膜增殖性疾病,是儿童失明的主要原因。需要准确方便的自动诊断工具来帮助眼科医生诊断ROP。现有的方法只从后角拍摄的眼底图像中提取信息,而忽略了从其他角度拍摄的图像,这限制了算法的性能。在本文中,我们提出了一种基于图的特征融合网络(GFF-Net),该网络可以联合分析多个图像,并充分利用这些图像之间的相关信息来诊断ROP中的加号疾病。不同眼底图像的卷积特征被连接到图中,其中图的边缘对这些图像之间的相关性进行建模。提出了一种基于图的特征融合模块,从构建的特征图中聚合特征并生成最终预测。我们在临床数据集和低质量的“攻击数据集”上将所提出的GFF-Net与最先进的方法进行了比较。与其他方法相比,GFF-Net在这两个数据集上都取得了优异的性能。结果表明,在临床实践中,所提出的GF F-Net可能比现有方法更有效。
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来源期刊
Applied Intelligence
Applied Intelligence 工程技术-计算机:人工智能
CiteScore
6.60
自引率
20.80%
发文量
1361
审稿时长
5.9 months
期刊介绍: With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.
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