Retinal Vessel Segmentation with Masked Auto Encoders

We explore retinal vessel segmentation under scarce annotations with self-supervised learning. A masked autoencoder first learns domain-specific retinal structure from abundant unlabeled fundus images. We then transfer the pretrained encoder to downstream tasks—principally vessel segmentation—by fine-tuning on a small labeled set, achieving finer vessel recovery.