{"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/detecting-and-classifying-lesions-in","title":"Detecting and classifying lesions in mammograms with Deep Learning","arxiv_id":"1707.08401","date":"2017-07-26","proceeding":null,"authors":["Dezső Ribli","Anna Horváth","Zsuzsa Unger","Péter Pollner","István Csabai"],"abstract":"In the last two decades Computer Aided Diagnostics (CAD) systems were\ndeveloped to help radiologists analyze screening mammograms. The benefits of\ncurrent CAD technologies appear to be contradictory and they should be improved\nto be ultimately considered useful. Since 2012 deep convolutional neural\nnetworks (CNN) have been a tremendous success in image recognition, reaching\nhuman performance. These methods have greatly surpassed the traditional\napproaches, which are similar to currently used CAD solutions. Deep CNN-s have\nthe potential to revolutionize medical image analysis. We propose a CAD system\nbased on one of the most successful object detection frameworks, Faster R-CNN.\nThe system detects and classifies malignant or benign lesions on a mammogram\nwithout any human intervention. The proposed method sets the state of the art\nclassification performance on the public INbreast database, AUC = 0.95 . The\napproach described here has achieved the 2nd place in the Digital Mammography\nDREAM Challenge with AUC = 0.85 . When used as a detector, the system reaches\nhigh sensitivity with very few false positive marks per image on the INbreast\ndataset. Source code, the trained model and an OsiriX plugin are availaible\nonline at https://github.com/riblidezso/frcnn_cad .","url_abs":"http://arxiv.org/abs/1707.08401v3","url_pdf":"http://arxiv.org/pdf/1707.08401v3.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":"detecting-and-classifying-lesions-in","repo_url":"https://github.com/riblidezso/frcnn_cad","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"breast-cancer-detection","task_name":"Breast Cancer Detection"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"medical-image-analysis","task_name":"Medical Image Analysis"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"faster-r-cnn","method_name":"Faster R-CNN"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"roipool","method_name":"RoIPool"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1707.08401","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}