  The progression of breast cancer can be quantified in lymph node whole-slide
images (WSIs). We describe a novel method for effectively performing
classification of whole-slide images and patient level breast cancer grading.
Our method utilises a deep neural network. The method performs classification
on small patches and uses model averaging for boosting. In the first step,
region of interest patches are determined and cropped automatically by color
thresholding and then classified by the deep neural network. The classification
results are used to determine a slide level class and for further aggregation
to predict a patient level grade. Fast processing speed of our method enables
high throughput image analysis.
