The Center for Biomedical Informatics and Information Technology of the National Cancer Institute (NCI) presented a virtual Medical Imaging De-Identification (MIDI) workshop focused on public sharing of imaging data.
The primary emphasis of the workshop was on medical images with accompanying data elements, especially those in formats in which the data elements are embedded, particularly DICOM.
The goals of the two-day workshop were to:
- Share best practices and recommendations for medical imaging de-identification, as identified by the MIDI Task Group convened by the NCI.
- Learn about approaches to conventional image de-identification in the United States, the European Union, and Canada.
- Discuss approaches to image de-identification by industry.
- Explore the roles of statistical risk analysis, de-facing, and AI in de-identification.
Recordings
| Date | Slides | Recording |
|---|---|---|
| 5/22/2023 | ||
| 5/23/2023 | Day 2 Recording |
Slide Presentations
The workshop agenda and speaker bios are on the workshop website.
Some documents on this page are not Section 508 compliant. To receive a compliant document, please email NCI CBIIT MIDI Group.
| Speaker | Presentation Title | Slides |
|---|---|---|
| Day 1 | ||
| Keyvan Farahani, PhD, National Heart, Lung, and Blood Institute, National Institutes of Health | Welcome & Introduction | |
| Session 1: Report of the MIDI Task Group - Best Practices and Recommendations | ||
| David Clunie, MBBS, PixelMed | Report of the MIDI Task Group | |
Session 2: Tools for Conventional Approaches to De-Identification Chairperson, Fred Prior, PhD | ||
| Fred Prior, PhD | Setting the Stage | |
| Michael Rutherford, MS, University of Arkansas for Medical Sciences | The Tools of TCIA: Standardizing Zero-Tolerance De-identification | |
| Stephen Moore, MS, Washington University School of Medicine in St. Louis | XNAT Platform: Image De-identification | |
Session 3: International Approaches to De-Identification Chairperson: William Parker, University of British Columbia | ||
| William Parker, MD | Medical Data De-ID - A Canadian Perspective | Parker Slides |
| Haridimos Kondylakis, PhD, Institute of Computer Science, Foundation of Research & Technology (FORTH) | Data Infrastructures for AI in Medical Imaging: A report on the experiences of five EU projects | |
| Christian Ludwigs, MSc, Aigora GmbH | Legal framework and best practices for medical image de-identification in the EU | |
Session 4: Industry Panel on Image De-Identification Chairperson: Juergen Klenk, PhD, Deloitte Consulting | ||
| Juergen Klenk, PhD | Introductory Remarks to the Industry Panel | |
| Bob Lou, MD, Google | Medical imaging de-identification on both images and text using AI models | |
| Lawrence (Tony) O’Sullivan, MS, IBIS | Optimizing and Automating Radiology Data De-identification Workflows | |
| Dan Marcus, PhD, Flywheel | The Flywheel Platform for Intelligent Image Anonymization | |
| Jiri Dobes, PhD, John Snow Labs | Automated Medical Data De-Identification and Obfuscation | |
| Abraham Gutman, MS, AG Mednet, Inc. | Advances in Medical Imaging De-Identification and the Impact of Regulatory Constraints | |
| Day 2 | ||
| Session 5: Pathology Whole Slide Image De-Identification | ||
| Adam Taylor, PhD, Sage Bionetworks | Pathology Whole Slide Image De-Identification | |
| Tom Bisson, PhD, Charité Universitätsmedizin Berlin | Anonymization of Whole Slide Images in in Histopathology for Research and Education | |
| David Gutman, MD, PhD, Emory University | Image DePHI and the DSA: Open Source tools for Histology Image DeIdentification | |
| Session 6: De-Facing | De-Facing | |
| Ying Xiao,PhD, Hospital of the University of Pennsylvania | Medical Image De-Facing and Clinical Research Data Sharing | |
| Christopher Schwarz, PhD, Mayo Clinic | Face Recognition and De-Identification of Research Brain Images with mri_reface | |
| Douglas Greve, PhD, MGH/Harvard | MIDEFACE: Minimally Invasive Defacing | |
| Session 7: The Role of AI in Image De-Identification | ||
| Judy Gichoya, MD, Emory University | ||
| George Shih, MD, Weill Cornell Medical College | Pixel De-Identification Using AI | |
| Adrienne Kline, MD, PhD, Northwestern University | PyLogik: An open-source resource for medical image de-identification | |
| Session 8: NCI MIDI Datasets and Pipeline | ||
| Keyvan Farahani, PhD | The Medical Image De-Identification Initiative (MIDI) | |
| Fred Prior, PhD | Synthetic Data for De-Identification Testing - The MIDI Datasets | |
| Ben Kopchick, PhD, Deloitte Consulting | Building a cloud-based MIDI pipeline |
