{"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/leveraging-deep-steins-unbiased-risk","title":"Leveraging Deep Stein's Unbiased Risk Estimator for Unsupervised X-ray Denoising","arxiv_id":"1811.12488","date":"2018-11-29","proceeding":null,"authors":["Fahad Shamshad","Muhammad Awais","Muhammad Asim","Zain ul Aabidin Lodhi","Muhammad Umair","Ali Ahmed"],"abstract":"Among the plethora of techniques devised to curb the prevalence of noise in\nmedical images, deep learning based approaches have shown the most promise.\nHowever, one critical limitation of these deep learning based denoisers is the\nrequirement of high-quality noiseless ground truth images that are difficult to\nobtain in many medical imaging applications such as X-rays. To circumvent this\nissue, we leverage recently proposed approach of [7] that incorporates Stein's\nUnbiased Risk Estimator (SURE) to train a deep convolutional neural network\nwithout requiring denoised ground truth X-ray data. Our experimental results\ndemonstrate the effectiveness of SURE based approach for denoising X-ray\nimages.","url_abs":"http://arxiv.org/abs/1811.12488v1","url_pdf":"http://arxiv.org/pdf/1811.12488v1.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":[{"paper_slug":"leveraging-deep-steins-unbiased-risk","repo_url":"https://github.com/awaisrauf/xray-denoising","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"denoising","task_name":"Denoising"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}