SynFundus-1M: A High-quality Million-scale Synthetic fundus images Dataset with Fifteen Types of Annotation

arXiv:2312.00377v4 Announce Type: replace
Abstract: 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 …

abstract annotation annotations arxiv concerns cost cs.ai cs.cv data data privacy dataset datasets images imaging intelligent medical medical imaging paper privacy public quality release research scale synthetic terms type types