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Across the 47 reviewed studies, ViT-based architectures matched or exceeded CNN baselines on classification of retinal pathologies[12][28], while remaining sensitive to pre-training scale.
However, performance gains diminished on smaller annotated corpora — a pattern consistent with findings on cross-domain transfer[34].
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Masked autoencoding (MAE) tends to outperform contrastive methods on segmentation when labelled data is scarce[3], largely because the reconstruction objective preserves fine-grained spatial detail. Contrastive pretraining[5]remains stronger for classification of well-curated cohorts.
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