Histographs: Graphs in Histopathology

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 multi-relational 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 multi-relational 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.

Automated summary

In this study, cancers were classified using graph convolutional networks by modeling a tissue section as a multi-attributed multi-relational spatial graph of its constituent cells. The approach demonstrated competitive classification accuracy with CNNs on the BACH breast cancer dataset, specifically in distinguishing cancerous versus non-cancerous and in situ versus invasive cases.