Deep Learning for Identifying Metastatic Breast Cancer

Citation:

Wang D, Khosla A, Gargeya R, Irshad H, Beck AH. Deep Learning for Identifying Metastatic Breast Cancer [Internet]. arXiv 2016;
1606.05718v1.pdf8.8 MB

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.

Publisher's Version

Last updated on 06/20/2016