{"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-for-identifying-metastatic","title":"Deep Learning for Identifying Metastatic Breast Cancer","arxiv_id":"1606.05718","date":"2016-06-18","proceeding":null,"authors":["Dayong Wang","Aditya Khosla","Rishab Gargeya","Humayun Irshad","Andrew H. Beck"],"abstract":"The International Symposium on Biomedical Imaging (ISBI) held a grand\nchallenge to evaluate computational systems for the automated detection of\nmetastatic breast cancer in whole slide images of sentinel lymph node biopsies.\nOur team won both competitions in the grand challenge, obtaining an area under\nthe receiver operating curve (AUC) of 0.925 for the task of whole slide image\nclassification and a score of 0.7051 for the tumor localization task. A\npathologist independently reviewed the same images, obtaining a whole slide\nimage classification AUC of 0.966 and a tumor localization score of 0.733.\nCombining our deep learning system's predictions with the human pathologist's\ndiagnoses increased the pathologist's AUC to 0.995, representing an\napproximately 85 percent reduction in human error rate. These results\ndemonstrate the power of using deep learning to produce significant\nimprovements in the accuracy of pathological diagnoses.","url_abs":"http://arxiv.org/abs/1606.05718v1","url_pdf":"http://arxiv.org/pdf/1606.05718v1.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-for-identifying-metastatic","repo_url":"https://github.com/3dimaging/DeepLearningCamelyon","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"deep-learning-for-identifying-metastatic","repo_url":"https://github.com/DIDSR/DeepLearningCamelyon_II","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"deep-learning-for-identifying-metastatic","repo_url":"https://github.com/TheSaintIndiano/Bamboo-Forest-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"},{"task_slug":"whole-slide-images","task_name":"whole slide images"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.05718","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}