Heterogeneous Graphs Model Spatial Relationships Between Biological Entities for Breast Cancer Diagnosis
Published in Workshop on GRaphs in biomedicAl Image anaLysis, International Conference on Medical Image Computing and Computer Assisted Intervention, 2023
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.
Recommended citation: Krishna, A., Gupta, R.K., Kurian, N.C., Jeevan, P., Sethi, A. (2024). Heterogeneous Graphs Model Spatial Relationship Between Biological Entities for Breast Cancer Diagnosis. In: Ahmadi, SA., Pereira, S. (eds) Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology. MICCAI 2023. Lecture Notes in Computer Science, vol 14373. Springer, Cham. https://doi.org/10.1007/978-3-031-55088-1_9