端到端细胞检测：基于实例感知图建模

中文摘要 AI 辅助

准确的细胞检测和分类对于病理分析至关重要，直接影响诊断准确性和治疗计划。为了捕捉肿瘤微环境中超越视觉外观的复杂细胞相互作用，已有多种方法采用图神经网络对细胞核之间的空间和关系模式进行建模，并取得了有前景的结果。然而，这些方法通常采用两阶段范式，即先进行视觉特征提取，再进行关系建模，这需要对每个阶段进行单独调优，从而增加了流程的复杂性并阻碍了端到端的联合优化。在本文中，我们提出了一种用于细胞检测和分类的端到端框架，该框架联合建模补丁级视觉表示和实例级交互，其中包含一个动态图构建模块和一个实例感知图网络。具体而言，图构建模块使用从补丁级特征中派生的可学习查询作为细胞实例表示来动态构建图结构，其邻接关系通过整合特征相似性和空间距离来定义。实例感知图网络执行自适应实例过滤和特征重组，将它们聚合到细胞图上的拓扑潜在状态中，以进行由视觉线索驱动的选择性状态空间转换，融合外观和关系证据。在多个采用不同染色方案的细胞和细胞核检测数据集上进行评估时，我们的方法在检测和分类性能上均显著优于现有方法。代码将在该https URL发布。

英文摘要

Accurate cell detection and classification are crucial for pathological analysis, directly affecting diagnostic accuracy and treatment planning. To capture complex cellular interactions beyond visual appearance within the tumor microenvironment, several approaches have employed graph neural networks to model spatial and relational patterns among cell nuclei, yielding promising results. However, these methods typically adopt a two-stage paradigm of visual extraction followed by relational modeling, which necessitates separate tuning for each stage, thereby increasing pipeline complexity and hindering end-to-end joint optimization. In this paper, we propose an end-to-end framework for cell detection and classification that jointly models patch-level visual representations and instance-level interactions, which incorporates a dynamic graph construction module and an instance-aware graph network. Specifically, the graph construction module dynamically builds the graph structure using learnable queries derived from patch-level features as cell instance representations, with adjacency defined by integrating feature similarity and spatial distances. The instance-aware graph network performs adaptive instance filtering and feature reorganization, aggregating them over the cell graph into a topological latent state for a selective state-space transition driven by visual cues, fusing appearance and relational evidence. When evaluated on multiple datasets with different staining protocols for cell and nucleus detection, our method significantly outperforms existing approaches in both detection and classification performance. The code will be released at https://github.com/RuochenLiu23/IGM.

发表机构

• University of Liverpool（利物浦大学）
• Zhejiang Normal University（浙江师范大学）
• Zhejiang University（浙江大学）
• Sun Yat-sen University（中山大学）

机构由 AI 辅助整理，请以论文原文为准。