{"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/thoracic-disease-identification-and","title":"Thoracic Disease Identification and Localization with Limited Supervision","arxiv_id":"1711.06373","date":"2017-11-17","proceeding":"CVPR 2018 6","authors":["Zhe Li","Chong Wang","Mei Han","Yuan Xue","Wei Wei","Li-Jia Li","Li Fei-Fei"],"abstract":"Accurate identification and localization of abnormalities from radiology\nimages play an integral part in clinical diagnosis and treatment planning.\nBuilding a highly accurate prediction model for these tasks usually requires a\nlarge number of images manually annotated with labels and finding sites of\nabnormalities. In reality, however, such annotated data are expensive to\nacquire, especially the ones with location annotations. We need methods that\ncan work well with only a small amount of location annotations. To address this\nchallenge, we present a unified approach that simultaneously performs disease\nidentification and localization through the same underlying model for all\nimages. We demonstrate that our approach can effectively leverage both class\ninformation as well as limited location annotation, and significantly\noutperforms the comparative reference baseline in both classification and\nlocalization tasks.","url_abs":"http://arxiv.org/abs/1711.06373v6","url_pdf":"http://arxiv.org/pdf/1711.06373v6.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":"thoracic-disease-identification-and","repo_url":"https://github.com/romanovar/evaluation_MIL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.06373","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}