Aim

At the cell-level, the distinction between normal and malignant epithelial cells is challenging due to the high morphologic heterogeneity of malignant cells. These cells vary a lot in shape, size and sometimes resemble normal epithelial cells. For that reason, common computer vision methods struggle to correctly differentiate epithelial cells into normal or malignant. In this project, we propose a new method based on the aggregation of local and global tissue features to take advantage of the surrounding gland morphology to accurately learn to distinguish these two epithelial classes.

Epithelial graphs were built to capture the epithelial tissue structure. Nodes were individual epithelial cells connected to their closest neighbours using Delaunay triangulation. Hidden embeddings extracted from a trained ResNet were used as node features. Graph Neural Networks (GNNs) were trained for node(cell) classification (normal vs malignant epithelial cell). A final post-processing step (graph clustering and median filtering) is applied to smooth predictions inside individual glands. The different classification steps can be visualized in the Figure below.