In digital pathology, the spatial context of cells is important for cell classification, cancer diagnosis and prognosis. To model such complex cell context, 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 topological data analysis. We incorporate such structural descriptors into a deep generative model as both conditional inputs and a differentiable loss. This way, we are able to generate high quality multi-class cell layouts for the first time. We show that the topology-rich cell layouts can be used for data augmentation and improve the performance of downstream tasks such as cell classification.

在数字病理学中，为了进行细胞分类、癌症诊断和预后，细胞的空间上下文信息至关重要。本研究引入了空间统计和拓扑数据分析中的数学工具，将这些结构描述符引入深度生成模型，作为条件输入和可微损失，来生成高质量的多类细胞布局，并证明了拓扑丰富的细胞布局可以用于数据增强和提高细胞分类等下游任务的性能。

数字病理学中基于拓扑的多类细胞上下文生成