text: 334,72,1427210038347714561,1214477029053214720,Jan Kukačka 🇺🇦,"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 <LINK> 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 <LINK> The improvement was greater in few-shot scenarios. Moreover, the pre-trained networks converged significantly faster. 4/6 <LINK> 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 👩‍🔬 Anja whose master's thesis is behind large portion of this paper! 6/6",https://arxiv.org/abs/2108.02798,"Fundus photography is the primary method for retinal imaging and essential for diabetic retinopathy prevention. Automated segmentation of fundus photographs would improve the quality, capacity, and cost-effectiveness of eye care screening programs. However, current segmentation methods are not robust towards the diversity in imaging conditions and pathologies typical for real-world clinical applications. To overcome these limitations, we utilized contrastive self-supervised learning to exploit the large variety of unlabeled fundus images in the publicly available EyePACS dataset. We pre-trained an encoder of a U-Net, which we later fine-tuned on several retinal vessel and lesion segmentation datasets. We demonstrate for the first time that by using contrastive self-supervised learning, the pre-trained network can recognize blood vessels, optic disc, fovea, and various lesions without being provided any labels. Furthermore, when fine-tuned on a downstream blood vessel segmentation task, such pre-trained networks achieve state-of-the-art performance on images from different datasets. Additionally, the pre-training also leads to shorter training times and an improved few-shot performance on both blood vessel and lesion segmentation tasks. Altogether, our results showcase the benefits of contrastive self-supervised pre-training which can play a crucial role in real-world clinical applications requiring robust models able to adapt to new devices with only a few annotated samples. ","Self-Supervised Learning from Unlabeled Fundus Photographs Improves