# Cells are Actors: Social Network Analysis with Classical ML for SOTA Histology Image Classification

arXiv: https://arxiv.org/abs/2106.15299 · HTML: https://mlsanity.com/paper/2106.15299/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 29 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Deep neural network models for computational histopathology: A survey](https://mlsanity.com/paper/1912.12378/) (2019)
- [Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations](https://mlsanity.com/paper/2008.05571/) (2020)
- [CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images](https://mlsanity.com/paper/1909.01068/) (2019)
- [CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation](https://mlsanity.com/paper/1903.05358/) (2019)
- [NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images](https://mlsanity.com/paper/2005.14511/) (2020)
- [Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images](https://mlsanity.com/paper/1907.09478/) (2019)
- [Improving Whole Slide Segmentation Through Visual Context - A Systematic Study](https://mlsanity.com/paper/1806.04259/) (2018)
- [Deconvolving convolution neural network for cell detection](https://mlsanity.com/paper/1806.06970/) (2018)

## Built on by (most cited first, 5 of 5)

- [Domain Generalization in Computational Pathology: Survey and Guidelines](https://mlsanity.com/paper/2310.19656/) (2023)
- [SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology](https://mlsanity.com/paper/2312.15010/) (2023)
- [Robust Interactive Semantic Segmentation of Pathology Images with Minimal User Input](https://mlsanity.com/paper/2108.13368/) (2021)
- [Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications](https://mlsanity.com/paper/2202.08916/) (2022)
- [PA-MIL: Phenotype-Aware Multiple Instance Learning Guided by Language Prompting and Genotype-to-Phenotype Relationships](https://mlsanity.com/paper/2602.02558/) (2026)

## Abstract (arXiv)

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.