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• Introduction to new potential task group member, David Brundage, professor at Cornell.
• Whole-slide images (WSI) are not usually in DICOM format and must be converted.
• Dave Gutman shared slides about protected health information in WSI.
• Most PHI lives in the slide label. Sometimes only a partial label is scanned, so a human might not realize PHI is there, but a machine can detect that PHI.
• The primary image is unlikely to contain PHI. 
• Luke Geneslaw also shared some slides about detecting label presence in tissue image scans.
• Trade-off between missing tissue that has cancer on it and bigger files that could have more PHI in the data.
• Leaving data in the slide label causes problems for de-id. There isn't software out there to redaction of pixel data from the tissue sample. Dave Gutman is working on an NCI project to develop it.
• JPEG stores data in 8x8 blocks, so it's possible to remove individual blocks from an image.
• Metadata extraction is unique to format.
• TCIA has a dictionary of private data elements from DICOM.
• Python package: https://github.com/DigitalSlideArchive/tifftools
• Date can be in TIFF times and other data elements defined in the XML, or included as an annotation. 
• It's our job to identify which areas need to be mitigated.
• Not all slides are standard formats, like prostate whole mounts.
• David Clunie would like to create a sub-group of people with a special interest and experience in WSI so that they can create content on that subject for the report. Fred Prior, Dave Gutman, David Brundage will join.
• We need a person with significant statistical knowledge who could adapt their knowledge to defacing. Justin suggested someone and will talk to David about it.
• David shared the new version of the task group's de-id report. Please follow the tracked changes offline and if you have any comments on it, please let him know.
• Common stratification based on type–the task group determined that this is not sufficient and we should be looking at everything regardless. Sentence added to report.
• We have not yet defined a best practice on how to score risk. Further research is needed.

June 14, 2022 Meeting

Publication Publications shared by David Clunie :  Schwarz Schwarz CG, Kremers WK, Lowe VJ, Savvides M, Gunter JL, Senjem ML, et al. Face recognition from research brain PET: An Unexpected PET Problem: PET Face Recognition. NeuroImage. 2022 Jun 3;119357. doi:10.1016/j.neuroimage.2022.119357

https://nvlpubs.nist.gov/nistpubs/Legacy/IR/nistir7408.pdf doi:10.6028/NIST.IR.7408