Study on the Method of Fundus Image Generation Based on Improved GAN

Abstract

<jats:p>With the continuous development of deep learning, the performance of the intelligent diagnosis system for ocular fundus diseases has been significantly improved, but during the system training process, problems like lack of fundus samples and uneven sample distribution (the number of disease samples is much smaller than the number of normal samples) have become increasingly prominent. In view of the previous issues, this paper proposes a method for generating fundus images based on “Combined GAN” (Com-GAN), which can generate both normal fundus images and fundus images with hard exudates, so that the sample distribution can be more even, while the fundus data are expanded. First, this paper uses existing images to train a Com-GAN, which consists of two subnetworks: im-WGAN and im-CGAN; then, it uses the trained model to generate fundus images, then performs qualitative and quantitative evaluation on the generated images, and adds the images to the original image set to expand the datasets; finally, based on this expanded training set, it trains the hard exudate detection system. The expanded datasets effectively improve the generalization ability of the system on the public datasets DIARETDB1 and e-ophtha EX, thereby verifying the effectiveness of the proposed method.</jats:p>

An Annotation-free Restoration Network for Cataractous Fundus Images

“…For the annotated private dataset, the evaluation metrics of structural similarity (SSIM) and peak signal to noise ratio (PSNR) were used to quantify the restored image quality. While for the unannotated public dataset, the evaluation metrics of image quality, i.e., inception scores (IS) and natural image quality evaluator (NIQE) [39], [48], were adopted to assess the restoration performance.…”

Comparative analysis of CycleGAN and AttentionGAN on face aging application

“…With the problem of training GAN and its evaluation, wide space is present in the research community to explore GAN. Guo et al 2020 [17] had proposed a GAN called combined GAN (Com-GAN) and done the study using improved GAN for generating fundus images. It had shown high-quality results in comparison to other generative models.…”

Redundancy Removal Adversarial Active Learning Based on Norm Online Uncertainty Indicator

“…By taking advantage of the image distance, a core-set approach [37] can select a set of data points from an unlabelled dataset and obtain a result that a model learned from the selected subset that is competitive for the remaining data points. VAAL [22] uses the adversarial learning [23,24] of a variational autoencoder (VAE) [38] and discriminator to learn the feature representations of labelled samples and unlabelled samples and then uses the difference between them to make a sample selection. In essence, the method selects samples based on their diversity, which is not equal to the amount of information contained in a sample, so the results of the method may be unreliable.…”

“…The purpose of the synthesis-based methods is to synthesize the most useful samples for the model by using the generated model [ 24 , 39 ]. The idea was first proposed in GAAL [ 25 ], which uses a GAN to generate samples closer to the decision boundary than the existing samples.…”

“…e recently proposed SRAAL method [21] uses annotated information and labelled/unlabelled state information to select samples and has achieved competitive performance. SRAAL inherited VAAL's idea of adversarial learning [22]; that is, SRAAL uses the adversarial approach [23,24] to learn the feature representation of labelled samples and unlabelled samples and selects samples that can increase the diversity of the labelled pool according to the distribution of samples. In addition, SRAAL sets up an online uncertainty indicator (OUI) for the unlabelled samples to calculate the contribution of this sample to the model.…”