Re-identification of patients from imaging features extracted by foundation models.
Authors
Affiliations (8)
Affiliations (8)
- Ophthalmology Department, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
- MGH & BWH Center for Clinical Data Science, Mass General Brigham, Somerville, MA, USA.
- MPH, Ophthalmology, Oregon Health & Science University, Portland, OR, USA.
- National Eye Institute, National Institutes of Health, Bethesda, MD, USA.
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
- Moorfields Eye Hospital, London, UK.
- Ophthalmology Department, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. [email protected].
Abstract
Foundation models for medical imaging are a prominent research topic, but risks associated with the imaging features they can capture have not been explored. We aimed to assess whether imaging features from foundation models enable patient re-identification and to relate re-identification to demographic features prediction. Our data included Colour Fundus Photos (CFP), Optical Coherence Tomography (OCT) b-scans, and chest x-rays and we reported re-identification rates of 40.3%, 46.3%, and 25.9%, respectively. We reported varying performance on demographic features prediction depending on re-identification status (e.g., AUC-ROC for gender from CFP is 82.1% for re-identified images vs. 76.8% for non-re-identified ones). When training a deep learning model on the re-identification task, we reported performance of 82.3%, 93.9%, and 63.7% at image level on our internal CFP, OCT, and chest x-ray data. We showed that imaging features extracted from foundation models in ophthalmology and radiology include information that can lead to patient re-identification.