HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification

TL;DR

This paper introduces HACT-Net, a hierarchical graph neural network that models cell and tissue structures to improve breast cancer subtype classification from histopathological images.

Contribution

It proposes a novel hierarchical cell-to-tissue graph representation and a corresponding neural network, enhancing tissue modeling for cancer classification.

Findings

Outperformed recent CNN and GNN methods in breast cancer subtyping

Hierarchical modeling aligns with pathological diagnostic procedures

Provides better control over tissue representation incorporating pathological priors

Abstract

Cancer diagnosis, prognosis, and therapeutic response prediction are heavily influenced by the relationship between the histopathological structures and the function of the tissue. Recent approaches acknowledging the structure-function relationship, have linked the structural and spatial patterns of cell organization in tissue via cell-graphs to tumor grades. Though cell organization is imperative, it is insufficient to entirely represent the histopathological structure. We propose a novel hierarchical cell-to-tissue-graph (HACT) representation to improve the structural depiction of the tissue. It consists of a low-level cell-graph, capturing cell morphology and interactions, a high-level tissue-graph, capturing morphology and spatial distribution of tissue parts, and cells-to-tissue hierarchies, encoding the relative spatial distribution of the cells with respect to the tissue distribution. Further, a hierarchical graph neural network (HACT-Net) is proposed to efficiently map the HACT representations to histopathological breast cancer subtypes. We assess the methodology on a large set of annotated tissue regions of interest from H\&E stained breast carcinoma whole-slides. Upon evaluation, the proposed method outperformed recent convolutional neural network and graph neural network approaches for breast cancer multi-class subtyping. The proposed entity-based topological analysis is more inline with the pathological diagnostic procedure of the tissue. It provides more command over the tissue modelling, therefore encourages the further inclusion of pathological priors into task-specific tissue representation.