论文标题
黑暗而明亮的渠道先验引导的深层网络，用于视网膜图像质量评估
A Dark and Bright Channel Prior Guided Deep Network for Retinal Image Quality Assessment
论文作者
论文摘要
视网膜图像质量评估是视网膜疾病诊断的重要任务。最近，有一些新兴的视网膜图像质量的深层模型。当前的最新技术要么直接传输最初是为自然图像设计的，要么通过多个CNN分支或独立的CNN引入视网膜图像的质量分类或引入额外的图像质量先验。本文提出了一个黑暗和明亮的通道先验引导的深层网络，以用于视网膜图像质量评估，称为guidednet。具体而言，黑暗和明亮的通道先验嵌入到网络的开始层中，以提高深度特征的区分能力。此外，我们重新注释了一个称为RIQA-RFMID的新的视网膜图像质量数据集，以进行进一步验证。公共图像质量数据集的眼睛质量以及我们重新注册的数据集RIQA-RFMID的实验结果证明了拟议的Guidednet的有效性。
Retinal image quality assessment is an essential task in the diagnosis of retinal diseases. Recently, there are emerging deep models to grade quality of retinal images. Current state-of-the-arts either directly transfer classification networks originally designed for natural images to quality classification of retinal images or introduce extra image quality priors via multiple CNN branches or independent CNNs. This paper proposes a dark and bright channel prior guided deep network for retinal image quality assessment called GuidedNet. Specifically, the dark and bright channel priors are embedded into the start layer of network to improve the discriminate ability of deep features. In addition, we re-annotate a new retinal image quality dataset called RIQA-RFMiD for further validation. Experimental results on a public retinal image quality dataset Eye-Quality and our re-annotated dataset RIQA-RFMiD demonstrate the effectiveness of the proposed GuidedNet.
