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In digital pathology, the spatial context of cells is important for cellclassification, cancer diagnosis and prognosis. To model such complex cellcontext, however, is challenging. Cells form different mixtures, lineages,clusters and holes. To model such structural patterns in a learnable fashion,we introduce several mathematical tools from spatial statistics and topologicaldata analysis. We incorporate such structural descriptors into a deepgenerative model as both conditional inputs and a differentiable loss. Thisway, we are able to generate high quality multi-class cell layouts for thefirst time. We show that the topology-rich cell layouts can be used for dataaugmentation and improve the performance of downstream tasks such as cellclassification.

Topology-Guided Multi-Class Cell Context Generation for Digital Pathology