SSL-Unet: A Self-Supervised Learning Strategy Base on U-Net for Retinal Vessel Segmentation

We propose a SSL-Unet model for retinal vascular segmentation as well as two self-supervised training strategies. The strategy can help the self-supervised module to learn pseudo labels for improving the segmentation performance. Moreover, the fusion of both self-supervised and supervised paradigms is applied to retinal segmentation for the first time. Meanwhile, it can also be extended to any segmentation network.