The batch effect is a nonbiological variation that arises from technical differences across different batches of data during the data generation process for acquisition-related reasons, such as collection of images at different sites or using different scanners. This phenomenon can affect the robustness and generalizability of computational pathology- or radiology-based cancer diagnostic models, especially in multi-center studies. To address this issue, we developed an open-source platform, Batch Effect Explorer (BEEx), that is designed to qualitatively and quantitatively determine whether batch effects exist among medical image datasets from different sites. A suite of tools was incorporated into BEEx that provide visualization and quantitative metrics based on intensity, gradient, and texture features to allow users to determine whether there are any image variables or combinations of variables that can distinguish datasets from different sites in an unsupervised manner. BEEx was designed to support various medical imaging techniques, including microscopy and radiology. Four use cases clearly demonstrated the ability of BEEx to identify batch effects and validated the effectiveness of rectification methods for batch effect reduction. Overall, BEEx is a scalable and versatile framework designed to read, process, and analyze a wide range of medical images to facilitate the identification and mitigation of batch effects, which can enhance the reliability and validity of image-based studies.

中文翻译：

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BEEx 是一种开源工具，可评估医学图像中的批量效应以实现多中心研究


批次效应是一种非生物性变化，是由于数据生成过程中由于采集相关原因（例如在不同站点收集图像或使用不同的扫描仪）而引起的不同批次数据的技术差异。这种现象会影响基于计算病理学或放射学的癌症诊断模型的稳健性和泛化性，尤其是在多中心研究中。为了解决这个问题，我们开发了一个开源平台，即 Batch Effect Explorer （BEEx），该平台旨在定性和定量地确定来自不同站点的医学图像数据集中是否存在批量效应。BEEx 中集成了一套工具，这些工具提供基于强度、梯度和纹理特征的可视化和定量指标，使用户能够确定是否有任何图像变量或变量组合可以无监督方式区分来自不同站点的数据集。BEEx 旨在支持各种医学成像技术，包括显微镜和放射学。四个用例清楚地展示了 BEEx 识别批量效应的能力，并验证了减少批量效应的整流方法的有效性。总体而言，BEEx 是一个可扩展的多功能框架，旨在读取、处理和分析各种医学图像，以促进识别和减轻批量效应，从而提高基于图像的研究的可靠性和有效性。