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.

翻译：在数字病理学中，细胞的空间上下文对细胞分类、癌症诊断及预后至关重要。然而，建模这种复杂的细胞上下文具有挑战性。细胞会形成不同的混合体、谱系、簇和空洞。为了以可学习的方式建模这些结构模式，我们引入了来自空间统计学和拓扑数据分析的若干数学工具。我们将这些结构描述符作为条件输入和可微损失函数，整合到深度生成模型中。通过这种方式，我们首次能够生成高质量的多类细胞布局。我们证明，富含拓扑信息的细胞布局可用于数据增强，并提升细胞分类等下流任务的性能。