National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Shenzhen Eye Hospital, Shenzhen Key Ophthalmic Laboratory, Health Science Center, Shenzhen University, The Second Affiliated Hospital of Jinan University, Shenzhen, China.
Med Image Anal. 2021 Jul;71:102031. doi: 10.1016/j.media.2021.102031. Epub 2021 Mar 10.
Fundus diseases classification is vital for the health of human beings. However, most of existing methods detect diseases by means of single angle fundus images, which lead to the lack of pathological information. To address this limitation, this paper proposes a novel deep learning method to complete different fundus diseases classification tasks using ultra-wide field scanning laser ophthalmoscopy (SLO) images, which have an ultra-wide field view of 180-200˚. The proposed deep model consists of multi-branch network, atrous spatial pyramid pooling module (ASPP), cross-attention and depth-wise attention module. Specifically, the multi-branch network employs the ResNet-34 model as the backbone to extract feature information, where the ResNet-34 model with two-branch is followed by the ASPP module to extract multi-scale spatial contextual features by setting different dilated rates. The depth-wise attention module can provide the global attention map from the multi-branch network, which enables the network to focus on the salient targets of interest. The cross-attention module adopts the cross-fusion mode to fuse the channel and spatial attention maps from the ResNet-34 model with two-branch, which can enhance the representation ability of the disease-specific features. The extensive experiments on our collected SLO images and two publicly available datasets demonstrate that the proposed method can outperform the state-of-the-art methods and achieve quite promising classification performance of the fundus diseases.
眼底疾病分类对人类健康至关重要。然而，现有的大多数方法都是通过单角度眼底图像来检测疾病，这导致缺乏病理信息。针对这一局限性，本文提出了一种新的深度学习方法，使用超广角扫描激光检眼镜（SLO）图像来完成不同的眼底疾病分类任务，该方法具有 180-200˚的超广角视野。所提出的深度模型由多分支网络、空洞空间金字塔池化模块（ASPP）、交叉注意力和深度注意力模块组成。具体来说，多分支网络采用 ResNet-34 模型作为骨干网络来提取特征信息，其中，ResNet-34 模型的两个分支后面跟着 ASPP 模块，通过设置不同的扩张率来提取多尺度空间上下文特征。深度注意力模块可以从多分支网络中提供全局注意力图，使网络能够关注感兴趣的显著目标。交叉注意力模块采用交叉融合模式融合来自两个分支的 ResNet-34 模型的通道和空间注意力图，从而增强疾病特定特征的表示能力。在我们收集的 SLO 图像和两个公开可用数据集上的广泛实验表明，所提出的方法可以优于最先进的方法，并实现相当有前景的眼底疾病分类性能。
