{"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/low-dose-ct-with-a-residual-encoder-decoder","title":"Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)","arxiv_id":"1702.00288","date":"2017-02-01","proceeding":null,"authors":["Hu Chen","Yi Zhang","Mannudeep K. Kalra","Feng Lin","Yang Chen","Peixi Liao","Jiliu Zhou","Ge Wang"],"abstract":"Given the potential X-ray radiation risk to the patient, low-dose CT has\nattracted a considerable interest in the medical imaging field. The current\nmain stream low-dose CT methods include vendor-specific sinogram domain\nfiltration and iterative reconstruction, but they need to access original raw\ndata whose formats are not transparent to most users. Due to the difficulty of\nmodeling the statistical characteristics in the image domain, the existing\nmethods for directly processing reconstructed images cannot eliminate image\nnoise very well while keeping structural details. Inspired by the idea of deep\nlearning, here we combine the autoencoder, the deconvolution network, and\nshortcut connections into the residual encoder-decoder convolutional neural\nnetwork (RED-CNN) for low-dose CT imaging. After patch-based training, the\nproposed RED-CNN achieves a competitive performance relative to\nthe-state-of-art methods in both simulated and clinical cases. Especially, our\nmethod has been favorably evaluated in terms of noise suppression, structural\npreservation and lesion detection.","url_abs":"http://arxiv.org/abs/1702.00288v3","url_pdf":"http://arxiv.org/pdf/1702.00288v3.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":"low-dose-ct-with-a-residual-encoder-decoder","repo_url":"https://github.com/SSinyu/RED_CNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"lesion-detection","task_name":"Lesion Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}