Deep Learning-based Patient Re-identification Is able to Exploit the Biometric Nature of Medical Chest X-ray Data

• Main Authors: Packhäuser, Kai, Gündel, Sebastian, Münster, Nicolas, Syben, Christopher, Christlein, Vincent, Maier, Andreas
• Format: Journal Article
• Language: English
• Published: 02-09-2022
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning
• DOI: 10.48550/arxiv.2103.08562

Scientific Reports, 12, Article number: 14851 (2022) With the rise and ever-increasing potential of deep learning techniques in
recent years, publicly available medical datasets became a key factor to enable
reproducible development of diagnostic algorithms in the medical domain.
Medical data contains sensitive patient-related information and is therefore
usually anonymized by removing patient identifiers, e.g., patient names before
publication. To the best of our knowledge, we are the first to show that a
well-trained deep learning system is able to recover the patient identity from
chest X-ray data. We demonstrate this using the publicly available large-scale
ChestX-ray14 dataset, a collection of 112,120 frontal-view chest X-ray images
from 30,805 unique patients. Our verification system is able to identify
whether two frontal chest X-ray images are from the same person with an AUC of
0.9940 and a classification accuracy of 95.55%. We further highlight that the
proposed system is able to reveal the same person even ten and more years after
the initial scan. When pursuing a retrieval approach, we observe an mAP@R of
0.9748 and a precision@1 of 0.9963. Furthermore, we achieve an AUC of up to
0.9870 and a precision@1 of up to 0.9444 when evaluating our trained networks
on external datasets such as CheXpert and the COVID-19 Image Data Collection.
Based on this high identification rate, a potential attacker may leak
patient-related information and additionally cross-reference images to obtain
more information. Thus, there is a great risk of sensitive content falling into
unauthorized hands or being disseminated against the will of the concerned
patients. Especially during the COVID-19 pandemic, numerous chest X-ray
datasets have been published to advance research. Therefore, such data may be
vulnerable to potential attacks by deep learning-based re-identification
algorithms.