  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 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 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.
