# Histographs: Graphs in Histopathology

arXiv: https://arxiv.org/abs/1908.05020 · HTML: https://mlsanity.com/paper/1908.05020/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2019.

## Built on by (most cited first, 20 of 20)

- [Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks](https://mlsanity.com/paper/2107.13048/) (2021)
- [Trustworthy Graph Neural Networks: Aspects, Methods and Trends](https://mlsanity.com/paper/2205.07424/) (2022)
- [Graph Representation Learning in Biomedicine](https://mlsanity.com/paper/2104.04883/) (2021)
- [Hierarchical Graph Representations in Digital Pathology](https://mlsanity.com/paper/2102.11057/) (2021)
- [A Survey on Graph-Based Deep Learning for Computational Histopathology](https://mlsanity.com/paper/2107.00272/) (2021)
- [HistoCartography: A Toolkit for Graph Analytics in Digital Pathology](https://mlsanity.com/paper/2107.10073/) (2021)
- [A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection](https://mlsanity.com/paper/2205.10014/) (2022)
- [Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review](https://mlsanity.com/paper/2303.06471/) (2023)
- [Multi stain graph fusion for multimodal integration in pathology](https://mlsanity.com/paper/2204.12541/) (2022)
- [Cell Graph Transformer for Nuclei Classification](https://mlsanity.com/paper/2402.12946/) (2024)
- [Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications](https://mlsanity.com/paper/2202.08916/) (2022)
- [Towards the Explanation of Graph Neural Networks in Digital Pathology with Information Flows](https://mlsanity.com/paper/2112.09895/) (2021)
- [Focus on Focus: Focus-oriented Representation Learning and Multi-view Cross-modal Alignment for Glioma Grading](https://mlsanity.com/paper/2408.08527/) (2024)
- [A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphs](https://mlsanity.com/paper/2211.06292/) (2022)
- [Heterogeneous graphs model spatial relationships between biological entities for breast cancer diagnosis](https://mlsanity.com/paper/2307.08132/) (2023)
- [Leveraging Medical Foundation Model Features in Graph Neural Network-Based Retrieval of Breast Histopathology Images](https://mlsanity.com/paper/2405.04211/) (2024)
- [GNNFormer: A Graph-based Framework for Cytopathology Report Generation](https://mlsanity.com/paper/2303.09956/) (2023)
- [Explainable and Position-Aware Learning in Digital Pathology](https://mlsanity.com/paper/2306.08198/) (2023)
- [Artifact-Robust Graph-Based Learning in Digital Pathology](https://mlsanity.com/paper/2310.18192/) (2023)
- [Adversary-Robust Graph-Based Learning of WSIs](https://mlsanity.com/paper/2403.14489/) (2024)

## Abstract (arXiv)

Spatial arrangement of cells of various types, such as tumor infiltrating lymphocytes and the advancing edge of a tumor, are important features for detecting and characterizing cancers. However, convolutional neural networks (CNNs) do not explicitly extract intricate features of the spatial arrangements of the cells from histopathology images. In this work, we propose to classify cancers using graph convolutional networks (GCNs) by modeling a tissue section as a multi-attributed spatial graph of its constituent cells. Cells are detected using their nuclei in H&E stained tissue image, and each cell's appearance is captured as a multi-attributed high-dimensional vertex feature. The spatial relations between neighboring cells are captured as edge features based on their distances in a graph. We demonstrate the utility of this approach by obtaining classification accuracy that is competitive with CNNs, specifically, Inception-v3, on two tasks-cancerous versus non-cancerous and in situ versus invasive-on the BACH breast cancer dataset.