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.
Documenting the de-identification process of clinical and imaging data for ai for health imaging projects
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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.