Heterogeneous graphs model spatial relationships between biological entities for breast cancer diagnosis

TL;DR

This paper introduces a heterogeneous graph neural network that models spatial and hierarchical relationships between cells and tissues in histopathological images, improving breast cancer diagnosis accuracy over existing transformer-based methods.

Contribution

It presents a novel heterogeneous GNN approach that captures interrelationships between biological entities, outperforming state-of-the-art models in breast cancer image classification.

Findings

Superior accuracy on three breast cancer datasets

More parameter-efficient than transformer models

Effective modeling of spatial and hierarchical relationships

Abstract

The heterogeneity of breast cancer presents considerable challenges for its early detection, prognosis, and treatment selection. Convolutional neural networks often neglect the spatial relationships within histopathological images, which can limit their accuracy. Graph neural networks (GNNs) offer a promising solution by coding the spatial relationships within images. Prior studies have investigated the modeling of histopathological images as cell and tissue graphs, but they have not fully tapped into the potential of extracting interrelationships between these biological entities. In this paper, we present a novel approach using a heterogeneous GNN that captures the spatial and hierarchical relations between cell and tissue graphs to enhance the extraction of useful information from histopathological images. We also compare the performance of a cross-attention-based network and a transformer architecture for modeling the intricate relationships within tissue and cell graphs. Our model demonstrates superior efficiency in terms of parameter count and achieves higher accuracy compared to the transformer-based state-of-the-art approach on three publicly available breast cancer datasets -- BRIGHT, BreakHis, and BACH.