标题： 通过合成DR1图像生成增强早期糖尿病视网膜病变检测：一种StyleGAN3方法 显示英文标题

标题： Enhancing Early Diabetic Retinopathy Detection through Synthetic DR1 Image Generation: A StyleGAN3 Approach

摘要： 糖尿病视网膜病变（DR）是可预防性失明的主要原因之一。早期在DR1阶段的检测至关重要，但由于高质量眼底图像的稀缺而受到阻碍。本研究使用StyleGAN3生成具有微动脉瘤特征且高保真度和多样性的合成DR1图像。目的是解决数据稀缺问题并提高有监督分类器的性能。使用了2602张DR1图像的数据集来训练模型，随后通过定量指标进行了全面评估，包括 弗雷切 inception 距离（FID）、核 inception 距离（KID），以及 平移（EQ-T）和旋转（EQ-R）的等变性。 定性评估包括人类图灵测试，受过培训的眼科医生评估了合成图像的真实性。光谱分析进一步验证了图像质量。 该模型最终达到了17.29的FID得分，优于从自助重抽样得到的平均FID 21.18（95%置信区间 - 20.83至 21.56）。 人类图灵测试表明该模型能够生成高度逼真的图像，但注意到边界的轻微伪影。 这些发现表明，由StyleGAN3生成的合成DR1图像对于增强训练数据集具有重要意义，能够实现更准确的糖尿病视网膜病变早期检测。这种方法突显了合成数据在推进医学影像和人工智能驱动诊断中的潜力。 显示英文摘要

摘要： Diabetic Retinopathy (DR) is a leading cause of preventable blindness. Early detection at the DR1 stage is critical but is hindered by a scarcity of high-quality fundus images. This study uses StyleGAN3 to generate synthetic DR1 images characterized by microaneurysms with high fidelity and diversity. The aim is to address data scarcity and enhance the performance of supervised classifiers. A dataset of 2,602 DR1 images was used to train the model, followed by a comprehensive evaluation using quantitative metrics, including Frechet Inception Distance (FID), Kernel Inception Distance (KID), and Equivariance with respect to translation (EQ-T) and rotation (EQ-R). Qualitative assessments included Human Turing tests, where trained ophthalmologists evaluated the realism of synthetic images. Spectral analysis further validated image quality. The model achieved a final FID score of 17.29, outperforming the mean FID of 21.18 (95 percent confidence interval - 20.83 to 21.56) derived from bootstrap resampling. Human Turing tests demonstrated the model's ability to produce highly realistic images, though minor artifacts near the borders were noted. These findings suggest that StyleGAN3-generated synthetic DR1 images hold significant promise for augmenting training datasets, enabling more accurate early detection of Diabetic Retinopathy. This methodology highlights the potential of synthetic data in advancing medical imaging and AI-driven diagnostics.