  Deep learning has been shown to be useful to detect breast cancer metastases
by analyzing whole slide images of sentinel lymph nodes. However, it requires
extensive scanning and analysis of all the lymph nodes slides for each case.
Our deep learning study focuses on breast cancer screening with only a small
set of image patches from any sentinel lymph node, positive or negative for
metastasis, to detect changes in tumor environment and not in the tumor itself.
We design a convolutional neural network in the Python language to build a
diagnostic model for this purpose. The excellent results from this preliminary
study provided a proof of concept for incorporating automated metastatic screen
into the digital pathology workflow to augment the pathologists' productivity.
Our approach is unique since it provides a very rapid screen rather than an
exhaustive search for tumor in all fields of all sentinel lymph nodes.
