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Abstract #123613 Published in IGR 25-1
Vision transformers: The next frontier for deep learning-based ophthalmic image analysis
Wu JH; Koseoglu ND; Jones C; Liu TYASaudi Journal of Ophthalmology 2023; 37: 173-178
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.
Department of Ophthalmology, Shiley Eye Institute and Viterbi Family, University of California, San Diego, La Jolla, CA, USA.
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