{"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/breast-mass-classification-from-mammograms","title":"Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks","arxiv_id":"1612.00542","date":"2016-12-02","proceeding":null,"authors":["Daniel Lévy","Arzav Jain"],"abstract":"Mammography is the most widely used method to screen breast cancer. Because\nof its mostly manual nature, variability in mass appearance, and low\nsignal-to-noise ratio, a significant number of breast masses are missed or\nmisdiagnosed. In this work, we present how Convolutional Neural Networks can be\nused to directly classify pre-segmented breast masses in mammograms as benign\nor malignant, using a combination of transfer learning, careful pre-processing\nand data augmentation to overcome limited training data. We achieve\nstate-of-the-art results on the DDSM dataset, surpassing human performance, and\nshow interpretability of our model.","url_abs":"http://arxiv.org/abs/1612.00542v1","url_pdf":"http://arxiv.org/pdf/1612.00542v1.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":"breast-mass-classification-from-mammograms","repo_url":"https://github.com/Clawton92/Classification_mammograms_cnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}