目的 提出一种基于人类视觉注意力机制的FA-Net网络结构以使卷积神经网络(CNN)更适用于眼病筛查系统中的图像质量评估.方法 FA-Net主网络由VGG-19网络组成,本研究在该基础上将人类视觉注意力机制加入到CNN中,并在训练时使用迁移学习的方法,使用ImageNet的权重初始化网络.注意力网络采用前景提取的方法,提取血管和疑似病灶点等感兴趣区域,并赋予感兴趣区域更高的权重来加强对感兴趣区域的学习.结果 在训练FA-Net时,使用了2894张眼底图像.FA-Net在包含2170张眼底图像的测试集上,分类准确率达97.65％,其敏感度和特异性分别为0.978和0.960,曲线下面积(AUC)为0.995.结论 FA-Net对比于其他CNN具有更优越的分类性能,能够更准确、高效地评估视网膜眼底图像质量.该网络考虑了人类视觉系统(HVS)和人类注意力机制,通过在VGG-19网络结构中加入注意力模块,在获得更好分类性能的同时也使分类结果更具有可解释性.%Objective To propose a deep learning-based retinal image quality classification network, FA-Net,to make convolutional neural network ( CNN) more suitable for image quality assessment in eye disease screening system. Methods The main network of FA-Net was composed of VGG-19. On this basis,attention mechanism was added to the CNN. By using transfer learning method in training, the weight of ImageNet was used to initialize the network. The attention net is based on foreground extraction by extracting the blood vessel and suspected regions of lesion and assigning higher weights to region of interest to enhance the learning of these important areas. Results Total of 2894 fundus images were used for training FA-Net. FA-Net achieved 97. 65％ classification accuracy on a test set containing 2170 fundus images,with the sensitivity and specificity of 0. 978 and 0. 960,respectively,and the area under curve(AUC) was 0. 995. Conclusions Compared with other CNNs,the proposed FA-Net has better classification performance and can evaluate retinal fundus image quality more accurately and efficiently. The network takes into account the human visual system ( HVS) and human attention mechanism. By adding attention module into the VGG-19 network structure, the classification results can be better interpreted as well as better classification performance.
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