  Digitization of histology images and the advent of new computational methods,
like deep learning, have helped the automatic grading of colorectal
adenocarcinoma cancer (CRA). Present automated CRA grading methods, however,
usually use tiny image patches and thus fail to integrate the entire tissue
micro-architecture for grading purposes. To tackle these challenges, we propose
to use a statistical network analysis method to describe the complex structure
of the tissue micro-environment by modelling nuclei and their connections as a
network. We show that by analyzing only the interactions between the cells in a
network, we can extract highly discriminative statistical features for CRA
grading. Unlike other deep learning or convolutional graph-based approaches,
our method is highly scalable (can be used for cell networks consist of
millions of nodes), completely explainable, and computationally inexpensive. We
create cell networks on a broad CRC histology image dataset, experiment with
our method, and report state-of-the-art performance for the prediction of
three-class CRA grading.
