Wu Jo-Hsuan, Koseoglu Neslihan D, Jones Craig, Liu T Y Alvin
Department of Ophthalmology, Shiley Eye Institute and Viterbi Family, University of California, San Diego, La Jolla, CA, USA.
Department of Ophthalmology, Wilmer Eye Institute, Johns Hopkins University, Baltimore, MD, USA.
Saudi J Ophthalmol. 2023 Jul 14;37(3):173-178. doi: 10.4103/sjopt.sjopt_91_23. eCollection 2023 Jul-Sep.
Deep learning is the state-of-the-art machine learning technique for ophthalmic image analysis, and convolutional neural networks (CNNs) are the most commonly utilized approach. Recently, vision transformers (ViTs) have emerged as a promising approach, one that is even more powerful than CNNs. In this focused review, we summarized studies that applied ViT-based models to analyze color fundus photographs and optical coherence tomography images. Overall, ViT-based models showed robust performances in the grading of diabetic retinopathy and glaucoma detection. While some studies demonstrated that ViTs were superior to CNNs in certain contexts of use, it is unclear how widespread ViTs will be adopted for ophthalmic image analysis, since ViTs typically require even more training data as compared to CNNs. The studies included were identified from the PubMed and Google Scholar databases using keywords relevant to this review. Only original investigations through March 2023 were included.
深度学习是用于眼科图像分析的最先进的机器学习技术，卷积神经网络（CNNs）是最常用的方法。最近，视觉Transformer（ViTs）已成为一种很有前景的方法，一种比CNN更强大的方法。在这篇重点综述中，我们总结了应用基于ViT的模型来分析彩色眼底照片和光学相干断层扫描图像的研究。总体而言，基于ViT的模型在糖尿病视网膜病变分级和青光眼检测方面表现出强大的性能。虽然一些研究表明，在某些使用场景中，ViTs优于CNNs，但尚不清楚ViTs在眼科图像分析中的应用会有多广泛，因为与CNNs相比，ViTs通常需要更多的训练数据。纳入的研究是从PubMed和谷歌学术数据库中使用与本综述相关的关键词识别出来的。仅纳入截至2023年3月的原始研究。
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