AMD-GAN: Attention encoder and multi-branch structure based generative adversarial networks for fundus disease detection from scanning laser ophthalmoscopy images

Abstract

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.