Large-scale public datasets with high-quality annotations are rarely available for intelligent medical imaging research, due to data privacy concerns and the cost of annotations. In this paper, we release SynFundus-1M, a high-quality synthetic dataset containing over one million fundus images in terms of \textbf{eleven disease types}. Furthermore, we deliberately assign four readability labels to the key regions of the fundus images. To the best of our knowledge, SynFundus-1M is currently the largest fundus dataset with the most sophisticated annotations. Leveraging over 1.3 million private authentic fundus images from various scenarios, we trained a powerful Denoising Diffusion Probabilistic Model, named SynFundus-Generator. The released SynFundus-1M are generated by SynFundus-Generator under predefined conditions. To demonstrate the value of SynFundus-1M, extensive experiments are designed in terms of the following aspect: 1) Authenticity of the images: we randomly blend the synthetic images with authentic fundus images, and find that experienced annotators can hardly distinguish the synthetic images from authentic ones. Moreover, we show that the disease-related vision features (e.g. lesions) are well simulated in the synthetic images. 2) Effectiveness for down-stream fine-tuning and pretraining: we demonstrate that retinal disease diagnosis models of either convolutional neural networks (CNN) or Vision Transformer (ViT) architectures can benefit from SynFundus-1M, and compared to the datasets commonly used for pretraining, models trained on SynFundus-1M not only achieve superior performance but also demonstrate faster convergence on various downstream tasks. SynFundus-1M is already public available for the open-source community.

翻译：由于数据隐私问题和标注成本，大规模高质量标注的公开数据集在智能医学影像研究中十分罕见。本文发布了SynFundus-1M，一个包含**十一种疾病类型**、超过一百万张高质量合成眼底图像的数据集。此外，我们还针对眼底图像关键区域精心分配了四种可读性标签。据我们所知，SynFundus-1M是目前规模最大且标注最为精细的眼底数据集。通过利用来自不同场景的超过130万张私有无污染真实眼底图像，我们训练了一个强大的去噪扩散概率模型，命名为SynFundus-Generator。发布的SynFundus-1M由SynFundus-Generator在预设条件下生成。为验证SynFundus-1M的价值，我们设计了以下方面的全面实验：1) 图像真实性：将合成图像与真实眼底图像随机混合，发现经验丰富的标注者几乎无法区分合成图像与真实图像。此外，我们证明合成图像中疾病相关的视觉特征（如病灶）得到了良好模拟。2) 对下游微调和预训练的有效性：我们证明无论是卷积神经网络（CNN）还是Vision Transformer（ViT）架构的视网膜疾病诊断模型，均能从SynFundus-1M中获益。与常用预训练数据集相比，基于SynFundus-1M训练的模型不仅在各项下游任务中性能更优，且收敛速度更快。SynFundus-1M现已向开源社区开放。