Cells are Actors: Social Network Analysis with Classical ML for SOTA Histology Image Classification
This addresses the challenge of integrating entire tissue structures for cancer grading, offering a scalable and explainable alternative to deep learning methods.
The paper tackled the problem of automated colorectal adenocarcinoma cancer grading by modeling tissue micro-architecture as a network of cell interactions, achieving state-of-the-art performance in three-class grading.
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.