用于眼底图像的鲁棒深度学习：连接真实数据与合成数据以增强泛化能力
Robust deep learning for eye fundus images: Bridging real and synthetic data for enhancing generalization
AI总结：
该研究比较十种 GAN 架构生成 AMD 眼底图像，并结合真实与合成数据训练 ResNet-18，在内部和外部验证中提升了检测准确率与泛化能力。
摘要：
用于评估医学图像的深度学习应用受到限制，因为数据集通常规模较小且不平衡。文献中已提出使用合成数据，但既没有报告对不同方法的鲁棒比较，也没有报告泛化能力。我们的方法整合了视网膜图像质量评估模型和 StyleGAN2 架构，以增强年龄相关性黄斑变性（AMD）检测能力并提高泛化性。本研究比较了十种不同的生成对抗网络（GAN）架构，以生成伴有和不伴有 AMD 的合成眼底图像。我们将三个公共数据库（iChallenge-AMD、ODIR-2019 和 RIADD）的子集组合起来，形成单一的训练集和测试集。我们采用 STARE 数据集进行外部验证，以确保对所提方法进行全面评估。结果表明，StyleGAN2 达到了最低的 Fréchet Inception Distance（166.17），且临床医生无法准确区分真实图像和合成图像。ResNet-18 架构取得了最佳性能，准确率为 85%，在检测 AMD 眼底图像方面优于两名人类专家（80% 和 75%）。测试集的准确率为 82.8%，STARE 数据集的准确率为 81.3%，证明了该模型的泛化能力。所提出的合成医学图像生成方法在鲁棒性和准确性方面均得到验证，其代码可免费获取，以促进该领域的进一步研究与开发。
英文摘要：
Deep learning applications for assessing medical images are limited because the datasets are often small and imbalanced. The use of synthetic data has been proposed in the literature, but neither a robust comparison of the different methods nor generalizability has been reported. Our approach integrates a retinal image quality assessment model and StyleGAN2 architecture to enhance Age-related Macular Degeneration (AMD) detection capabilities and improve generalizability. This work compares ten different Generative Adversarial Network (GAN) architectures to generate synthetic eye-fundus images with and without AMD. We combined subsets of three public databases (iChallenge-AMD, ODIR-2019, and RIADD) to form a single training and test set. We employed the STARE dataset for external validation, ensuring a comprehensive assessment of the proposed approach. The results show that StyleGAN2 reached the lowest Frechet Inception Distance (166.17), and clinicians could not accurately differentiate between real and synthetic images. ResNet-18 architecture obtained the best performance with 85% accuracy and outperformed the two human experts (80% and 75%) in detecting AMD fundus images. The accuracy rates were 82.8% for the test set and 81.3% for the STARE dataset, demonstrating the model's generalizability. The proposed methodology for synthetic medical image generation has been validated for robustness and accuracy, with free access to its code for further research and development in this field.
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