{"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/deep-learning-to-improve-breast-cancer-early","title":"Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography","arxiv_id":"1708.09427","date":"2017-08-30","proceeding":null,"authors":["Li Shen","Laurie R. Margolies","Joseph H. Rothstein","Eugene Fluder","Russell B. McBride","Weiva Sieh"],"abstract":"The rapid development of deep learning, a family of machine learning\ntechniques, has spurred much interest in its application to medical imaging\nproblems. Here, we develop a deep learning algorithm that can accurately detect\nbreast cancer on screening mammograms using an \"end-to-end\" training approach\nthat efficiently leverages training datasets with either complete clinical\nannotation or only the cancer status (label) of the whole image. In this\napproach, lesion annotations are required only in the initial training stage,\nand subsequent stages require only image-level labels, eliminating the reliance\non rarely available lesion annotations. Our all convolutional network method\nfor classifying screening mammograms attained excellent performance in\ncomparison with previous methods. On an independent test set of digitized film\nmammograms from Digital Database for Screening Mammography (DDSM), the best\nsingle model achieved a per-image AUC of 0.88, and four-model averaging\nimproved the AUC to 0.91 (sensitivity: 86.1%, specificity: 80.1%). On a\nvalidation set of full-field digital mammography (FFDM) images from the\nINbreast database, the best single model achieved a per-image AUC of 0.95, and\nfour-model averaging improved the AUC to 0.98 (sensitivity: 86.7%, specificity:\n96.1%). We also demonstrate that a whole image classifier trained using our\nend-to-end approach on the DDSM digitized film mammograms can be transferred to\nINbreast FFDM images using only a subset of the INbreast data for fine-tuning\nand without further reliance on the availability of lesion annotations. These\nfindings show that automatic deep learning methods can be readily trained to\nattain high accuracy on heterogeneous mammography platforms, and hold\ntremendous promise for improving clinical tools to reduce false positive and\nfalse negative screening mammography results.","url_abs":"http://arxiv.org/abs/1708.09427v5","url_pdf":"http://arxiv.org/pdf/1708.09427v5.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":"deep-learning-to-improve-breast-cancer-early","repo_url":"https://github.com/lishen/end2end-all-conv","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"deep-learning-to-improve-breast-cancer-early","repo_url":"https://github.com/aralab-unr/ga-mammograms","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"deep-learning-to-improve-breast-cancer-early","repo_url":"https://github.com/gkaposto/end2end_lishen","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"deep-learning-to-improve-breast-cancer-early","repo_url":"https://github.com/nyukat/mammography_metarepository","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"deep-learning-to-improve-breast-cancer-early","repo_url":"https://github.com/yuyuyu123456/CBIS-DDSM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"breast-cancer-detection","task_name":"Breast Cancer Detection"},{"task_slug":"cancer-no-cancer-per-image-classification","task_name":"Cancer-no cancer per image classification"},{"task_slug":"sensitivity","task_name":"Sensitivity"},{"task_slug":"specificity","task_name":"Specificity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/cancer-no-cancer-per-image-classification-on","task":"Cancer-no cancer per image classification","dataset":"CBIS-DDSM","model":"VGG/ResNet","rank_in_archive_order":12,"of":16,"metrics":{"AUC":"0.75"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}