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<strong>ABSTRACT</strong> Vision transformers are a type of deep learning model that has shown promising results in various computer vision tasks, including image classification, object detection, and segmentation. In the context of retinal imaging, vision transformers have been applied to various problems such as lesion detection, vessel segmentation, and optic disc and fovea localization. One major advantage of vision transformers is their ability to process input sequences of variable length, making them well-suited for tasks such as retinal image analysis where the size of the input images can vary significantly. In contrast to convolutional neural networks (CNNs), which typically require fixed-size input, vision transformers can process images of different sizes by using self-attention mechanisms to learn contextual relationships between different parts of the input sequence. Another advantage of vision transformers is their ability to handle long-range dependencies, which can be important in retinal imaging where the relationships between different structures within the retina can be complex and non-local. For example, vision transformers have been used to analyze retinal images to identify abnormalities such as diabetic retinopathy, which can be difficult to detect using traditional CNN-based approaches. In summary, vision transformers have shown great potential for applications in retinal imaging, with the ability to handle variable-sized inputs and long-range dependencies making them well-suited for tasks such as lesion detection and vessel segmentation. However, further research is needed to fully understand their capabilities and limitations in this context.