HistoColAi：一个集成AI驱动预测功能、用于协作式数字组织学图像标注的开源Web平台
HistoColAi: An Open-Source Web Platform for Collaborative Digital Histology Image Annotation with AI-Driven Predictive Integration
AI总结：
针对数字病理缺乏开放直观标注工具的问题，本文提出HistoColAi这一开源Web平台，支持组织学图像可视化、协作标注与AI预测集成，并通过梭形细胞皮肤肿瘤标注用例及可用性研究验证其可行性。
摘要：
数字病理学因其诸多优势已成为病理工作流程中的标准。这些优势包括所生成的全切片图像的细节水平，以及医院之间潜在的病例即时共享。基于深度学习的图像分析方法的最新进展，使其有望在数字病理学中提供辅助。然而，开发用于病理学的计算机辅助诊断系统的一个主要限制，是缺乏直观且开放的Web应用来进行数据标注。本文提出了一种Web服务，可高效提供用于可视化和标注数字化组织学图像的工具。此外，为展示并验证该工具，本文纳入了一个以多名标注者诊断梭形细胞皮肤肿瘤为中心的用例。文中还展示了该工具的可用性研究，结果表明所开发工具具有可行性。
英文摘要：
Digital pathology has become a standard in the pathology workflow due to its many benefits. These include the level of detail of the whole slide images generated and the potential immediate sharing of cases between hospitals. Recent advances in deep learning-based methods for image analysis make them of potential aid in digital pathology. However, a major limitation in developing computer-aided diagnostic systems for pathology is the lack of an intuitive and open web application for data annotation. This paper proposes a web service that efficiently provides a tool to visualize and annotate digitized histological images. In addition, to show and validate the tool, in this paper we include a use case centered on the diagnosis of spindle cell skin neoplasm for multiple annotators. A usability study of the tool is also presented, showing the feasibility of the developed tool.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
