{"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/csaw-m-an-ordinal-classification-dataset-for","title":"CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer","arxiv_id":"2112.01330","date":"2021-12-02","proceeding":null,"authors":["Moein Sorkhei","Yue Liu","Hossein Azizpour","Edward Azavedo","Karin Dembrower","Dimitra Ntoula","Athanasios Zouzos","Fredrik Strand","Kevin Smith"],"abstract":"Interval and large invasive breast cancers, which are associated with worse prognosis than other cancers, are usually detected at a late stage due to false negative assessments of screening mammograms. The missed screening-time detection is commonly caused by the tumor being obscured by its surrounding breast tissues, a phenomenon called masking. To study and benchmark mammographic masking of cancer, in this work we introduce CSAW-M, the largest public mammographic dataset, collected from over 10,000 individuals and annotated with potential masking. In contrast to the previous approaches which measure breast image density as a proxy, our dataset directly provides annotations of masking potential assessments from five specialists. We also trained deep learning models on CSAW-M to estimate the masking level and showed that the estimated masking is significantly more predictive of screening participants diagnosed with interval and large invasive cancers -- without being explicitly trained for these tasks -- than its breast density counterparts.","url_abs":"https://arxiv.org/abs/2112.01330v1","url_pdf":"https://arxiv.org/pdf/2112.01330v1.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":"csaw-m-an-ordinal-classification-dataset-for","repo_url":"https://github.com/yueliukth/csaw-m","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"csaw-m-an-ordinal-classification-dataset-for","repo_url":"https://github.com/moeinsorkhei/csaw-m_annotation_tool","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"ordinal-classification","task_name":"Ordinal Classification"},{"task_slug":"prognosis","task_name":"Prognosis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2112.01330","atlas_url":"https://app.syntology.ai/?focus=2112.01330","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}