# Deep Learning for Identifying Metastatic Breast Cancer Source: [Deep Learning for Identifying Metastatic Breast Cancer]() Collection index: [llms.txt]() Data updated: 2025-10-25T18:59:50.096Z Authors: Dayong Wang, Aditya Khosla, Rishab Gargeya, Humayun Irshad, Andrew H. Beck Published: 2016-06-18T04:00:31.000Z ## Abstract The International Symposium on Biomedical Imaging (ISBI) held a grand challenge to evaluate computational systems for the automated detection of metastatic breast cancer in whole slide images of sentinel lymph node biopsies. Our team won both competitions in the grand challenge, obtaining an area under the receiver operating curve (AUC) of 0.925 for the task of whole slide image classification and a score of 0.7051 for the tumor localization task. A pathologist independently reviewed the same images, obtaining a whole slide image classification AUC of 0.966 and a tumor localization score of 0.733. Combining our deep learning system's predictions with the human pathologist's diagnoses increased the pathologist's AUC to 0.995, representing an approximately 85 percent reduction in human error rate. These results demonstrate the power of using deep learning to produce significant improvements in the accuracy of pathological diagnoses. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [slide-level tumor localization on Camelyon 16 (test)](): slide-level tumor localization; Camelyon 16 (test); FROC: 80.7. ## Original sources and implementation [Original paper]() [PDF]()