text:   Segmentation of the Retina",6,"['Self-supervised learning has gained lots of attention recently. But what can a network really learn from unlabeled images of the retina? And is it useful for image segmentation? Good thing our new paper is out, with all the answers <LINK> 1/6 <LINK>', 'We used contrastive self-supervised learning to train a small convnet on Kaggle-DR dataset. Fascinatingly, without being provided any labels at all, the network learned to recognize various anatomical/pathological structures in the fundus images. 2/6 https://t.co/LNJpBotlax', 'Using this pre-trained network as the encoder of a U-Net led to improvements in image segmentation performance, compared to a U-Net trained from scratch. 3/6 https://t.co/yFjICKcS6Y', 'The improvement was greater in few-shot scenarios. Moreover, the pre-trained networks converged significantly faster. 4/6 https://t.co/rU9KAF3NhO', 'What is it good for? Glad you ask! It is impossible to have a large annotated dataset for every camera, pathology, and population. Self-supervised learning seems as an approach that could scale well with abundant unlabeled data and produce representations 5/6', ""which are robust and can be adapted with few annotated samples to new devices etc. Finally, big shout-out to 👩\u200d🔬 Anja whose master's thesis is behind large portion of this paper! 6/6""]",21,08,1244