ReviewProgress in retinal and eye research2026
Beyond convolutions and supervised learning with transformers and representation learning for retinal image analysis.
Review in Progress in retinal and eye research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
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Abstract
Retinal image analysis has enjoyed groundbreaking advances in the last ten years due to seismic improvements in image analysis techniques from the field of computer vision. Previous reviews in deep learning and artificial intelligence (AI) (Schmidt-Erfurth et al., 2018; Ting et al., 2019) have either focused on supervised learning, where labels are curated or manually created, or concentrated on the application of AI in specific image modalities and retina diseases (Hormel et al., 2021; Li et al., 2024a (Hormel et al., 2021; Li et al., 2024a)). In this review, we sought to summarize the advances in the field with the shift towards label-free approaches using representational learning and the emergence of vision transformers as alternatives to convolutional neural networks for image analysis. These advances include semi-supervised learning, self-supervised learning and directly led to the advent of foundation models, vision-language models, and multi-modal models.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.