mlsanityData to 14 Oct 2026

High performance on-demand de-identification of a petabyte-scale medical imaging data lake

arXiv 2008.01827 ↗First posted 4 Aug 2020cs.DC cs.PFcited by 3

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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.

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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 2.

3 papers.