High performance on-demand de-identification of a petabyte-scale medical imaging data lake
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Aug 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 2.
3 papers.