{"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/clearing-noisy-annotations-for-computed","title":"Clearing noisy annotations for computed tomography imaging","arxiv_id":"1807.09151","date":"2018-07-23","proceeding":null,"authors":["Roman Khudorozhkov","Alexander Koryagin","Alexey Kozhevin"],"abstract":"One of the problems on the way to successful implementation of neural\nnetworks is the quality of annotation. For instance, different annotators can\nannotate images in a different way and very often their decisions do not match\nexactly and in extreme cases are even mutually exclusive which results in noisy\nannotations and, consequently, inaccurate predictions.\n  To avoid that problem in the task of computed tomography (CT) imaging\nsegmentation we propose a clearing algorithm for annotations. It consists of 3\nstages:\n  - annotators scoring, which assigns a higher confidence level to better\nannotators;\n  - nodules scoring, which assigns a higher confidence level to nodules\nconfirmed by good annotators;\n  - nodules merging, which aggregates annotations according to nodules\nconfidence.\n  In general, the algorithm can be applied to many different tasks (namely,\nbinary and multi-class semantic segmentation, and also with trivial adjustments\nto classification and regression) where there are several annotators labeling\neach image.","url_abs":"http://arxiv.org/abs/1807.09151v1","url_pdf":"http://arxiv.org/pdf/1807.09151v1.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":"clearing-noisy-annotations-for-computed","repo_url":"https://github.com/analysiscenter/radio","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"computed-tomography-ct","task_name":"Computed Tomography (CT)"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}