HistoColAi:一个开源的网络平台,用于协作数字组织学图像注释与AI驱动的预测集成
Cristian Camilo Pulgarín-Ospina1, Rocío Del Amor1, Julio José Silva-Rodríguez2
1Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, Universitat Politècnica de València, Valencia, Spain.
Computer methods and programs in biomedicine
|January 15, 2025
概括
一个新的网络服务通过启用整个幻灯片图像 (WSI) 的注释和整合AI见解来简化数字病理学. 这种工具使得先进的深度学习可供病理学家使用,有助于诊断像状细胞皮肤瘤这样的疾病.
科学领域:
- 数字病理学数字病理学
- 计算病理学计算病理学
- 医疗图像分析 医学图像分析
背景情况:
- 数字病理学通过高细节全幻灯片图像 (WSI) 增强工作流程,并促进医院间病例共享.
- 深度学习的进步为病理学中的计算机辅助诊断提供了潜力.
- 一个关键的挑战是缺乏直观的,开源的 web 应用程序用于病理学数据注释.
研究的目的:
- 提出一个网络服务,以高效地可视化和注释数字化组织学图像.
- 将人工智能驱动的预测见解集成到病理学工作流程中.
- 为病理学家民主化深度学习模型的使用.
主要方法:
- 开发一个用于注释数字化组织学图像的网络服务,主要是TIFF格式的WSI.
- 整合人工智能驱动的预测洞察力.
- 通过一个使用案例进行演示,涉及诊断状细胞皮肤瘤.
- 进行可用性研究以评估可行性.
主要成果:
- 开发的网络服务有效地可视化和注释数字化组织学图像.
- 该工具整合了用于病理学应用的AI驱动的预测洞察力.
- 一项可用性研究证实了为病理学家开发的工具的可行性.
结论:
- 拟议的网络服务解决了数字病理学中数据注释的挑战.
- 该工具提高了病理学家深度学习模型的可访问性和可用性.
- 这种方法对改善诊断准确性和组织病理学效率充满希望.
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