Page History

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• Did you have a formal QC process that involved you verifying the quality of the de-identification process after it was done?
• John Perry: developed a process and a test to make sure it worked, but didn't look at all of the images to confirm it was done without breaches.
• Monitor logs to make sure nothing slips through without automation applied to it. Grab a random 1% and look through the headers.
• Need a more medical model that understands the variability in what we're trying to do
• Partial vs. complete success-field or header
• Catch-22 that you can't crowd-source because there could be PHI
• Build synthetic datasets that have real street addresses in real places that don't match the actual data
• Train a model and release that but not the dataset
• Would need a statistician
• Judy: We are encountering issues that the black box models do not understand. Running experiments on adversarial networks. Surprising findings.
• Amalgamate clinical and imaging data. 
• Models have already learned sufficient information to learn age, sex, and race. We don't understand how this happens and maybe they could pick up other identification data.
• We are not trying to hide age, sex, and race. We're trying to prevent the re-identification of a person.
• Increasing the uniqueness of the image data is a threat for re-identification. But if you don't have a database of everyone's fingerprints, for example, it's useless.
• At some point we have to be clear of what we are trying to reidentify and what the practical limits are.
• Clearview.ai

Tasking

• Justin Kirby: report back on what TCIA encounters that is part of their human review processes

• David Clunie: organize report topics in an outline

• Judy: Write up some content (not the overview) on defacing

• TJ and Ying: Can help with defacing

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