  Metastatic presence in lymph nodes is one of the most important prognostic
variables of breast cancer. The current diagnostic procedure for manually
reviewing sentinel lymph nodes, however, is very time-consuming and subjective.
Pathologists have to manually scan an entire digital whole-slide image (WSI)
for regions of metastasis that are sometimes only detectable under high
resolution or entirely hidden from the human visual cortex. From October 2015
to April 2016, the International Symposium on Biomedical Imaging (ISBI) held
the Camelyon Grand Challenge 2016 to crowd-source ideas and algorithms for
automatic detection of lymph node metastasis. Using a generalizable stain
normalization technique and the Proscia Pathology Cloud computing platform, we
trained a deep convolutional neural network on millions of tissue and tumor
image tiles to perform slide-based evaluation on our testing set of whole-slide
images images, with a sensitivity of 0.96, specificity of 0.89, and AUC score
of 0.90. Our results indicate that our platform can automatically scan any WSI
for metastatic regions without institutional calibration to respective stain
profiles.
