扫描激光检眼镜(SLO)已成为确定周围视网膜病变的重要工具,近年来。然而,采集到的SLO图像容易受到设备睫毛和框架的干扰,严重影响图像的关键特征提取。为了解决这个问题,我们提出了一种基于注意力编码器(AE)和多分支(MB)结构的称为AMD-GAN的生成对抗网络,用于从SLO图像中检测眼底疾病。具体来说,设计的发电机由两部分组成:AE和发电流网络,其中真实的SLO图像由AE模块编码以提取特征,生成流网络通过一系列残差块和上采样(RU)操作来处理随机高斯噪声,以生成与真实图像相同大小的假图像,其中AE还用于挖掘发电机的特征。对于鉴别器,通过复制ResNet-34模型的阶段3和阶段4结构来提取深层特征,设计了使用MB的ResNet网络。此外,利用深度方面的非对称扩张卷积来提取局部高级上下文特征并加速训练过程。此外,最后一层鉴别器被修改以构建分类器来检测病变和正常SLO图像。此外,利用专家的先验知识来提高检测结果。在两个局部SLO数据集上的实验结果表明,我们提出的方法在检测具有专家标记的患病和正常SLO图像方面是有前途的。
The scanning laser ophthalmoscopy (SLO) has become an important tool for the determination of peripheral retinal pathology, in recent years. However, the collected SLO images are easily interfered by the eyelash and frame of the devices, which heavily affect the key feature extraction of the images. To address this, we propose a generative adversarial network called AMD-GAN based on the attention encoder (AE) and multi-branch (MB) structure for fundus disease detection from SLO images. Specifically, the designed generator consists of two parts: the AE and generation flow network, where the real SLO images are encoded by the AE module to extract features and the generation flow network to handle the random Gaussian noise by a series of residual block with up-sampling (RU) operations to generate fake images with the same size as the real ones, where the AE is also used to mine features for generator. For discriminator, a ResNet network using MB is devised by copying the stage 3 and stage 4 structures of the ResNet-34 model to extract deep features. Furthermore, the depth-wise asymmetric dilated convolution is leveraged to extract local high-level contextual features and accelerate the training process. Besides, the last layer of discriminator is modified to build the classifier to detect the diseased and normal SLO images. In addition, the prior knowledge of experts is utilized to improve the detection results. Experimental results on the two local SLO datasets demonstrate that our proposed method is promising in detecting the diseased and normal SLO images with the experts labeling.