Digital pathology has gained significant traction in modern healthcare systems. This shift from optical microscopes to digital imagery brings with it the potential for improved diagnosis, efficiency, and the integration of AI tools into the pathologists workflow. A critical aspect of this is visualization. Throughout the development of a machine learning (ML) model in digital pathology, it is crucial to have flexible, openly available tools to visualize models, from their outputs and predictions to the underlying annotations and images used to train or test a model. We introduce TIAViz, a Python-based visualization tool built into TIAToolbox which allows flexible, interactive, fully zoomable overlay of a wide variety of information onto whole slide images, including graphs, heatmaps, segmentations, annotations and other WSIs. The UI is browser-based, allowing use either locally, on a remote machine, or on a server to provide publicly available demos. This tool is open source and is made available at: https://github.com/TissueImageAnalytics/tiatoolbox and via pip installation (pip install tiatoolbox) and conda as part of TIAToolbox.

翻译：数字病理学在现代医疗体系中日益重要。从光学显微镜向数字影像的转变，为提升诊断效率、优化诊断质量以及将AI工具整合至病理学家工作流程提供了可能。可视化正是这一过程中的关键环节。在数字病理学机器学习模型的开发全周期中，灵活开放的可视化工具至关重要——需支持对模型输出结果、预测结果、底层标注信息及训练/测试图像等多维度数据的可视化呈现。我们推出TIAViz——集成于TIAToolbox的Python可视化工具，可在全切片图像上实现灵活交互、无极缩放的多元信息叠加，包括图表、热力图、分割结果、标注信息及其他WSI数据。该工具采用基于浏览器的用户界面，既支持本地部署与远程机器运行，也可部署至服务器提供公开演示。本工具作为开源项目，可通过https://github.com/TissueImageAnalytics/tiatoolbox获取，并支持pip（pip install tiatoolbox）及conda安装方式集成至TIAToolbox。