Background and Objective:Computerized pathology image analysis is an important tool in research and clinical settings, which enables quantitative tissue characterization and can assist a pathologist’s evaluation. The aim of our study is to systematically quantify and minimize uncertainty in output of computer based pathology image analysis. Methods:Uncertainty quantification (UQ) and sensitivity analysis (SA) methods, such as Variance-Based Decomposition (VBD) and Morris One-At-a-Time (MOAT), are employed to track and quantify uncertainty in a real-world application with large Whole Slide Imaging datasets - 943 Breast Invasive Carcinoma (BRCA) and 381 Lung Squamous Cell Carcinoma (LUSC) patients. Because these studies are compute intensive, high-performance computing systems and efficient UQ/SA methods were combined to provide efficient execution. UQ/SA has been able to highlight parameters of the application that impact the results, as well as nuclear features that carry most of the uncertainty. Using this information, we built a method for selecting stable features that minimize application output uncertainty. Results: The results show that input parameter variations significantly impact all stages (segmentation, feature computation, and survival analysis) of the use case application. We then identified and classified features according to their robustness to parameter variation, and using the proposed features selection strategy, for instance, patient grouping stability in survival analysis has been improved from in 17% and 34% for BRCA and LUSC, respectively. Conclusions: This strategy created more robust analyses, demonstrating that SA and UQ are important methods that may increase confidence digital pathology.

背景和目的：计算机病理图像分析是研究和临床环境中的重要工具，它能够定量组织表征并有助于病理学家的评估。我们研究的目的是系统地量化和最小化基于计算机的病理图像分析输出的不确定性。方法：不确定性量化 (UQ) 和灵敏度分析 (SA) 方法，例如基于方差的分解 (VBD) 和 Morris One-At-a-Time (MOAT)，用于跟踪和量化具有大数据的实际应用中的不确定性。全玻片成像数据集 - 943 名乳腺浸润性癌 (BRCA) 和 381 名肺鳞状细胞癌 (LUSC) 患者。由于这些研究是计算密集型的，因此结合了高性能计算系统和高效的 UQ/SA 方法以提供高效的执行。UQ/SA 已经能够突出影响结果的应用参数，以及携带大部分不确定性的核特征。使用这些信息，我们构建了一种选择稳定特征的方法，以最大限度地减少应用程序输出的不确定性。结果：结果表明，输入参数变化显着影响用例应用程序的所有阶段（分割、特征计算和生存分析）。然后，我们根据特征对参数变化的鲁棒性来识别和分类特征，并使用所提出的特征选择策略，例如，生存分析中的患者分组稳定性分别从 BRCA 和 LUSC 的 17% 和 34% 提高。结论：该策略创建了更可靠的分析，证明 SA 和 UQ 是可以提高数字病理学可信度的重要方法。