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.
翻译：利用Fundus图像早期筛查眼病具有非常重要的临床意义,由于它的强大表现,深层学习在相关应用中越来越受欢迎,如损伤分解、生物标记分解、疾病诊断和图像合成,因此,非常有必要用一份审查文件总结Fundus图像深层学习的最新发展情况;在本次审查中,我们介绍了经过精心设计的143份应用文件;此外,还提供了33个可供公众查阅的数据集,为每项任务提供了可公开查阅的数据集;最后,披露了所有任务的共同限制,并提供了可能的解决办法;我们还将在https://github.com/nkicsl/Fundus Review上发布并定期更新最新结果和新发布的数据集,以适应该领域的快速发展。
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