{"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/content-noise-complementary-learning-for","title":"Content-Noise Complementary Learning for Medical Image Denoising","arxiv_id":null,"date":"2022-02-02","proceeding":"IEEE Transactions on Medical Imaging 2022 2","authors":["Mufeng Geng","Xiangxi Meng","Jiangyuan Yu","Lei Zhu","Lujia Jin","Zhe Jiang","Bin Qiu","Hui Li","Hanjing Kong","Jianmin Yuan","Kun Yang","Hongming Shan","Hongbin Han","Zhi Yang","Qiushi Ren","Yanye Lu"],"abstract":"Medical imaging denoising faces great challenges, yet is in great demand. With its distinctive characteristics, medical imaging denoising in the image domain requires innovative deep learning strategies. In this study, we propose a simple yet effective strategy, the content-noise complementary learning (CNCL) strategy, in which two deep learning predictors are used to learn the respective content and noise of the image dataset complementarily. A medical image denoising pipeline based on the CNCL strategy is presented, and is implemented as a generative adversarial network, where various representative networks (including U-Net, DnCNN, and SRDenseNet) are investigated as the predictors. The performance of these implemented models has been validated on medical imaging datasets including CT, MR, and PET. The results show that this strategy outperforms state-of-the-art denoising algorithms in terms of visual quality and quantitative metrics, and the strategy demonstrates a robust generalization capability. These findings validate that this simple yet effective strategy demonstrates promising potential for medical image denoising tasks, which could exert a clinical impact in the future. Code is available at: https://github.com/gengmufeng/CNCL-denoising.","url_abs":"https://pubmed.ncbi.nlm.nih.gov/34529565/","url_pdf":"https://pubmed.ncbi.nlm.nih.gov/34529565/","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":"content-noise-complementary-learning-for","repo_url":"https://github.com/gengmufeng/CNCL-denoising","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"content-noise-complementary-learning-for","repo_url":"https://github.com/kiananvari/Content-Noise-Complementary-Learning-for-Medical-Image-Denoising","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"image-denoising","task_name":"Image Denoising"},{"task_slug":"medical-image-denoising","task_name":"Medical Image 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}