A deep convolutional neural network for diabetic retinopathy detection via mining local and long-range dependence
A deep convolutional neural network for diabetic retinopathy detection via mining local and long-range dependence
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DOI：
10.1049/cit2.12155
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发表时间：
2023-01-24
影响因子：
5.1
通讯作者：
Zhang, David
中科院分区：
文献类型：
--
作者：
Luo, Xiaoling;Wang, Wei;Zhang, David
糖尿病视网膜病变（diabetic retinopathy，DR）是糖尿病最常见的并发症之一，是导致不可逆性失明的主要原因。目前，深度卷积神经网络在自动DR检测任务中已经取得了令人鼓舞的性能。方法的卷积运算是一种局部互相关运算，其感受野决定了用于处理的局部邻域的大小。然而，对于视网膜眼底照片，不仅存在局部信息，而且分散在整个图像中的病变特征（例如，渗出物和渗出物）之间存在长距离依赖性。所提出的方法将远程补丁之间的相关性纳入深度学习框架，以改善DR检测。由于DR的病变通常表现为斑块，因此使用逐块关系来增强局部斑块特征。所提出的网络中具有残差结构的远程单元可以灵活地嵌入到其他训练过的网络中。大量的实验结果表明，所提出的方法可以实现更高的准确性比现有的国家的最先进的Messidor和EyePACS数据集上的模型。
Diabetic retinopathy (DR), the main cause of irreversible blindness, is one of the most common complications of diabetes. At present, deep convolutional neural networks have achieved promising performance in automatic DR detection tasks. The convolution operation of methods is a local cross-correlation operation, whose receptive field determines the size of the local neighbourhood for processing. However, for retinal fundus photographs, there is not only the local information but also long-distance dependence between the lesion features (e.g. hemorrhages and exudates) scattered throughout the whole image. The proposed method incorporates correlations between long-range patches into the deep learning framework to improve DR detection. Patch-wise relationships are used to enhance the local patch features since lesions of DR usually appear as plaques. The Long-Range unit in the proposed network with a residual structure can be flexibly embedded into other trained networks. Extensive experimental results demonstrate that the proposed approach can achieve higher accuracy than existing state-of-the-art models on Messidor and EyePACS datasets.