  Convolutional Neural Network (CNN) models have become the state-of-the-art
for most computer vision tasks with natural images. However, these are not best
suited for multi-gigapixel resolution Whole Slide Images (WSIs) of histology
slides due to large size of these images. Current approaches construct smaller
patches from WSIs which results in the loss of contextual information. We
propose to capture the spatial context using novel Representation-Aggregation
Network (RAN) for segmentation purposes, wherein the first network learns
patch-level representation and the second network aggregates context from a
grid of neighbouring patches. We can use any CNN for representation learning,
and can utilize CNN or 2D-Long Short Term Memory (2D-LSTM) for
context-aggregation. Our method significantly outperformed conventional
patch-based CNN approaches on segmentation of tumour in WSIs of breast cancer
tissue sections.
