  The use of fundus images for the early screening of eye diseases is of great
clinical importance. Due to its powerful performance, deep learning is becoming
more and more popular in related applications, such as lesion segmentation,
biomarkers segmentation, disease diagnosis and image synthesis. Therefore, it
is very necessary to summarize the recent developments in deep learning for
fundus images with a review paper. In this review, we introduce 143 application
papers with a carefully designed hierarchy. Moreover, 33 publicly available
datasets are presented. Summaries and analyses are provided for each task.
Finally, limitations common to all tasks are revealed and possible solutions
are given. We will also release and regularly update the state-of-the-art
results and newly-released datasets at https://github.com/nkicsl/Fundus Review
to adapt to the rapid development of this field.
