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

arXiv: https://arxiv.org/abs/2008.01827 · HTML: https://mlsanity.com/paper/2008.01827/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2020.

## Built on by (most cited first, 3 of 3)

- [MedVAE: Efficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoders](https://mlsanity.com/paper/2502.14753/) (2025)
- [Medical Imaging and Machine Learning](https://mlsanity.com/paper/2103.01938/) (2021)
- [Image deidentification in the XNAT ecosystem: use cases and solutions](https://mlsanity.com/paper/2504.20657/) (2025)

## 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.