面向数字病理学的拓扑引导多类细胞上下文生成
Topology-Guided Multi-Class Cell Context Generation for Digital Pathology
AI总结：
本文提出将空间统计和拓扑数据分析的数学工具作为条件输入和可微损失整合到深度生成模型中，首次生成高质量多类细胞布局，用于数据增强并提升细胞分类等下游任务性能。
摘要：
在数字病理学中，细胞的空间上下文对细胞分类、癌症诊断和预后具有重要意义。然而，建模这种复杂的细胞上下文极具挑战性。细胞会形成不同的混合物、谱系、簇和孔洞。为了以可学习的方式建模此类结构模式，我们引入了来自空间统计和拓扑数据分析的多种数学工具。我们将这些结构描述符作为条件输入和可微损失整合到深度生成模型中。借此，我们首次能够生成高质量的多类细胞布局。我们证明，富含拓扑的细胞布局可用于数据增强，并提升细胞分类等下游任务的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
