{"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/ecg-segmentation-by-neural-networks-errors","title":"ECG Segmentation by Neural Networks: Errors and Correction","arxiv_id":"1812.10386","date":"2018-12-26","proceeding":null,"authors":["Iana Sereda","Sergey Alekseev","Aleksandra Koneva","Roman Kataev","Grigory Osipov"],"abstract":"In this study we examined the question of how error correction occurs in an\nensemble of deep convolutional networks, trained for an important applied\nproblem: segmentation of Electrocardiograms(ECG). We also explore the\npossibility of using the information about ensemble errors to evaluate a\nquality of data representation, built by the network. This possibility arises\nfrom the effect of distillation of outliers, which was demonstarted for the\nensemble, described in this paper.","url_abs":"http://arxiv.org/abs/1812.10386v1","url_pdf":"http://arxiv.org/pdf/1812.10386v1.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":"ecg-segmentation-by-neural-networks-errors","repo_url":"https://github.com/Namenaro/ecg_segmentation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"electrocardiography-ecg","task_name":"Electrocardiography (ECG)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}