发表机构
University of North Dakota; Google（北达科他大学; 谷歌）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出CPathOGen，一种条件潜在扩散框架，通过空间和形态控制生成H&E反事实，用于探测病理模型，并引入新指标评估模型对生物学因素与染色干扰的敏感性。
AI 中文摘要
计算病理模型从组织学图像中推断出具有生物学和临床意义的结果，但其预测受到复杂、交织的组织信号的影响，理解这些信号的作用至关重要。常见的像素和特征空间扰动可能产生不切实际的组织，使模型响应难以解释。我们提出了CPathOGen，一个条件潜在扩散框架，用于生成具有对细胞空间组织、核形态和染色外观显式控制的配对H&E反事实。细胞图谱通过空间编码器调节空间结构，而形态/外观向量通过分块特征级线性调制（FiLM）调节去噪过程。在保留的H&E图像块上，CPathOGen生成视觉上合理的组织，在空间引导选择后分布一致性得到改善，表现为更低的Fréchet Inception Distance（FID）和Kernel Inception Distance（KID）；生成的细胞追踪所要求的丰度和位置，测量的形态和颜色随其控制单调变化。我们使用这些经过验证的干预来探测带有端点头、任务特定分类器和生存模型的病理编码器。响应通过总变差距离和预测翻转率进行量化。我们进一步引入了生物学-干扰敏感性比率，该指标将模型对生物学动机的形态和空间因素的敏感性与对非生物学、染色相关的干扰变化的敏感性进行对比。CPathOGen为评估计算病理模型的鲁棒性和受控特征敏感性提供了一个实用、保真审计的框架。GitHub和Hugging Face。
英文摘要
Computational pathology models infer biologically and clinically meaningful outcomes from histology, but their predictions are shaped by complex, intertwined tissue signals whose roles are important to understand. Common pixel- and feature-space perturbations can produce implausible tissue, making model responses difficult to interpret. We introduce CPathOGen, a conditional latent-diffusion framework for generating paired H\&E counterfactuals with explicit controls over cellular spatial organization, nuclear morphology, and stain appearance. Cellular maps condition spatial structure through a spatial encoder, while a morphology/appearance vector modulates denoising through blockwise feature-wise linear modulation (FiLM). On held-out H\&E tiles, CPathOGen generates visually plausible tissue with improved distributional agreement after spatially guided selection, as reflected by lower Fréchet Inception Distance (FID) and Kernel Inception Distance (KID); generated cells track requested abundance and position, and measured morphology and color vary monotonically with their controls. We use these verified interventions to probe pathology encoders with endpoint heads, task-specific classifiers, and survival models. Responses are quantified using total variation distance and prediction-flip rate. We further introduce the Biology-Nuisance Sensitivity Ratio, a metric that contrasts model sensitivity to biologically motivated morphology and spatial factors with sensitivity to non-biological, stain-related nuisance variation. CPathOGen provides a practical, fidelity-audited framework for evaluating robustness and controlled feature sensitivity in computational pathology model. \href{https://github.com/a12dongithub/PathOGen}{GitHub} and \href{https://huggingface.co/a12donhf/CPathOGen}{Hugging~Face}.
