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High performance on-demand de-identification of a petabyte-scale medical imaging data lake
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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.
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Cited by 1 Pith paper
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Image deidentification in the XNAT ecosystem: use cases and solutions
A XNAT/DicomEdit pipeline scored 99.61% on the MIDI-B deidentification benchmark after test-set feedback, with remaining failures concentrated in addresses and burned-in pixels.
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