  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.
