Heterogeneous graphs model spatial relationships between biological
  entities for breast cancer diagnosis
Akhila Krishna K Ravi Kant Gupta Nikhil Cherian Kurian Pranav Jeevan Amit Sethi
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.