Re-identification from histopathology images

Ganz J, Ammeling J, Jabari S, Breininger K, Aubreville M (2025)

Publication Type: Journal article

Publication year: 2025

Journal

Medical Image Analysis Elsevier

Book Volume: 99

Article Number: 103335

DOI: 10.1016/j.media.2024.103335

Abstract

In numerous studies, deep learning algorithms have proven their potential for the analysis of histopathology images, for example, for revealing the subtypes of tumors or the primary origin of metastases. These models require large datasets for training, which must be anonymized to prevent possible patient identity leaks. This study demonstrates that even relatively simple deep learning algorithms can re-identify patients in large histopathology datasets with substantial accuracy. In addition, we compared a comprehensive set of state-of-the-art whole slide image classifiers and feature extractors for the given task. We evaluated our algorithms on two TCIA datasets including lung squamous cell carcinoma (LSCC) and lung adenocarcinoma (LUAD). We also demonstrate the algorithm's performance on an in-house dataset of meningioma tissue. We predicted the source patient of a slide with F1 scores of up to 80.1% and 77.19% on the LSCC and LUAD datasets, respectively, and with 77.09% on our meningioma dataset. Based on our findings, we formulated a risk assessment scheme to estimate the risk to the patient's privacy prior to publication.

Involved external institutions

Technische Hochschule Ingolstadt DE Germany (DE) Klinikum Nürnberg DE Germany (DE) Julius-Maximilians-Universität Würzburg DE Germany (DE)

How to cite

APA:

Ganz, J., Ammeling, J., Jabari, S., Breininger, K., & Aubreville, M. (2025). Re-identification from histopathology images. Medical Image Analysis, 99. https://doi.org/10.1016/j.media.2024.103335

MLA:

Ganz, Jonathan, et al. "Re-identification from histopathology images." Medical Image Analysis 99 (2025).

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