论文标题
pB尺度医学成像数据湖的高性能按需识别
High performance on-demand de-identification of a petabyte-scale medical imaging data lake
论文作者
论文摘要
随着人工智能驱动方法的增加，研究人员正在要求史无前例的医学成像数据，这些数据远远超过了传统的本地客户服务器方法的能力，以使数据研究准备就绪。我们正在为按需去识别的灵活解决方案提供，将成熟软件技术与现代基于云的分布式计算技术相结合，以使医学成像研究中的转换更快。该解决方案是一个更广泛的平台的一部分，该平台支持安全的高性能临床数据科学平台。
With the increase in Artificial Intelligence driven approaches, researchers are requesting unprecedented volumes of medical imaging data which far exceed the capacity of traditional on-premise client-server approaches for making the data research analysis-ready. We are making available a flexible solution for on-demand de-identification that combines the use of mature software technologies with modern cloud-based distributed computing techniques to enable faster turnaround in medical imaging research. The solution is part of a broader platform that supports a secure high performance clinical data science platform.
