使用 EyeGAN 生成眼底图像

Preeti Kapoor, Shaveta Arora
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摘要

深度学习模型被广泛应用于从分类、分割到识别等多个计算机视觉领域,但这些模型存在过度拟合的问题。数据集的多样化和平衡是解决这一主要问题的方法。生成对抗网络(GAN)是一种无监督学习的图像生成器,不需要任何额外信息。GAN 生成逼真的图像,并保留原始数据的微小细节。本文提出了一种用于生成眼底图像的 GAN 模型,以克服研究人员在检测和分类各种眼底疾病时面临的标记数据不足的问题。所提出的模型丰富并平衡了所研究的数据集,从而改进了眼科疾病检测系统。EyeGAN 是一种基于条件 GAN 的九层结构,它能生成无偏、优质、可信的图像,并以 226.3 的最小弗雷谢特起始距离(Fréchet Inception Distance)优于现有的 GAN 模型。公共眼底数据集 MESSIDOR I 和 MESSIDOR II 分别由 1600 和 808 幅合成图像扩展而成。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fundus Image Generation using EyeGAN
Deep learning models are widely used in various computer vision fields ranging from classification, segmentation to identification, but these models suffer from the problem of overfitting. Diversifying and balancing the datasets is a solution to the primary problem. Generative Adversarial Networks (GANs) are unsupervised learning image generators which do not require any additional information. GANs generate realistic images and preserve the minute details from the original data. In this paper, a GAN model is proposed for fundus image generation to overcome the problem of labelled data insufficiency faced by researchers in detection and classification of various fundus diseases. The proposed model enriches and balances the studied datasets for improving the eye disease detection systems. EyeGAN is a nine-layered structure based on conditional GAN which generates unbiased, good quality, credible images and outperforms the existing GAN models by achieving the least Fréchet Inception Distance of 226.3. The public fundus datasets MESSIDOR I and MESSIDOR II are expanded by 1600 and 808 synthetic images respectively.
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