{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/u2-net-a-bayesian-u-net-model-with-epistemic","title":"U2-Net: A Bayesian U-Net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans","arxiv_id":"1901.07929","date":"2019-01-23","proceeding":null,"authors":["José Ignacio Orlando","Philipp Seeböck","Hrvoje Bogunović","Sophie Klimscha","Christoph Grechenig","Sebastian Waldstein","Bianca S. Gerendas","Ursula Schmidt-Erfurth"],"abstract":"In this paper, we introduce a Bayesian deep learning based model for segmenting the photoreceptor layer in pathological OCT scans. Our architecture provides accurate segmentations of the photoreceptor layer and produces pixel-wise epistemic uncertainty maps that highlight potential areas of pathologies or segmentation errors. We empirically evaluated this approach in two sets of pathological OCT scans of patients with age-related macular degeneration, retinal vein oclussion and diabetic macular edema, improving the performance of the baseline U-Net both in terms of the Dice index and the area under the precision/recall curve. We also observed that the uncertainty estimates were inversely correlated with the model performance, underlying its utility for highlighting areas where manual inspection/correction might be needed.","url_abs":"https://arxiv.org/abs/1901.07929v2","url_pdf":"https://arxiv.org/pdf/1901.07929v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"image-matting","task_name":"Image Matting"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-matting-on-aim-500","task":"Image Matting","dataset":"AIM-500","model":"U2NET","rank_in_archive_order":4,"of":6,"metrics":{"Conn.":"82.14","Grad.":"51.02","MAD":"0.0493","MSE":"0.0348","SAD":"83.46"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1901.07929","atlas_url":"https://app.syntology.ai/?focus=1901.07929","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}