In recent years, advancements in retinal image analysis, driven by machine learning and deep learning techniques, have enhanced disease detection and diagnosis through automated feature extraction. However, challenges persist, including limited data set diversity due to privacy concerns and imbalanced sample pairs, hindering effective model training. To address these issues, we introduce the vessel and style guided generative adversarial network (VSG-GAN), an innovative algorithm building upon the foundational concept of GAN. In VSG-GAN, a generator and discriminator engage in an adversarial process to produce realistic retinal images. Our approach decouples retinal image generation into distinct modules: the vascular skeleton and background style. Leveraging style transformation and GAN inversion, our proposed hierarchical variational autoencoder module generates retinal images with diverse morphological traits. In addition, the spatially adaptive denormalization module ensures consistency between input and generated images. We evaluate our model on MESSIDOR and RITE data sets using various metrics, including structural similarity index measure, inception score, Fréchet inception distance, and kernel inception distance. Our results demonstrate the superiority of VSG-GAN, outperforming existing methods across all evaluation assessments. This underscores its effectiveness in addressing data set limitations and imbalances. Our algorithm provides a novel solution to challenges in retinal image analysis by offering diverse and realistic retinal image generation. Implementing the VSG-GAN augmentation approach on downstream diabetic retinopathy classification tasks has shown enhanced disease diagnosis accuracy, further advancing the utility of machine learning in this domain.

中文翻译：

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VSG-GAN：一种在视网膜眼底图像中进行语义操作的高保真图像合成方法

近年来，在机器学习和深度学习技术的推动下，视网膜图像分析的进步通过自动特征提取增强了疾病检测和诊断。然而，挑战仍然存在，包括由于隐私问题和样本对不平衡而导致数据集多样性有限，阻碍了有效的模型训练。为了解决这些问题，我们引入了血管和风格引导的生成对抗网络（VSG-GAN），这是一种基于 GAN 基本概念的创新算法。在 VSG-GAN 中，生成器和鉴别器参与对抗过程以生成逼真的视网膜图像。我们的方法将视网膜图像生成解耦为不同的模块：血管骨架和背景样式。利用风格变换和 GAN 反转，我们提出的分层变分自动编码器模块生成具有不同形态特征的视网膜图像。此外，空间自适应反规范化模块确保输入图像和生成图像之间的一致性。我们使用各种指标在 MESSIDOR 和 RITE 数据集上评估我们的模型，包括结构相似性指数测量、起始分数、Fréchet 起始距离和内核起始距离。我们的结果证明了 VSG-GAN 的优越性，在所有评估中都优于现有方法。这强调了它在解决数据集限制和不平衡方面的有效性。我们的算法通过提供多样化且逼真的视网膜图像生成，为视网膜图像分析中的挑战提供了一种新颖的解决方案。在下游糖尿病视网膜病变分类任务中实施 VSG-GAN 增强方法已显示出疾病诊断准确性的提高，进一步推进了机器学习在该领域的实用性。