Privacy-enhancing Image Sampling for the Synthesis of High-quality Anonymous Chest Radiographs

Packhäuser K, Folle L, Nguyen TT, Thamm F, Maier A (2024)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2024

Journal

Publisher: Springer Vieweg

Series: Informatik aktuell

City/Town: Wiesbaden

Pages Range: 27-32

Conference Proceedings Title: Bildverarbeitung für die Medizin 2024. BVM 2024

Event location: Erlangen

ISBN: 9783658440367

DOI: 10.1007/978-3-658-44037-4_12

Abstract

The development of well-performing deep learning-based algorithms for thoracic abnormality detection and classification relies on access to largescale chest X-ray datasets. However, the presence of patient-specific biometric information in chest radiographs impedes direct and public sharing of such data for research purposes due to the potential risk of patient re-identification. In this context, synthetic data generation emerges as a solution for anonymizing medical images. In this study, we utilize a privacy-enhancing sampling strategy within a latent diffusion model to generate fully anonymous chest radiographs.We conduct a comprehensive analysis of the employed method and examine the impact of different privacy degrees. For each configuration, the resulting synthetic images exhibit a substantial level of data utility, with only a marginal gap compared to real data. Qualitatively, a Turing test conducted with six radiologists confirms the high and realistic appearance of the generated chest radiographs, achieving an average classification accuracy of 55% across 50 images (25 real, 25 synthetic).

Authors with CRIS profile

Involved external institutions

Klinikum Fürth Germany (DE)

How to cite

APA:

Packhäuser, K., Folle, L., Nguyen, T.-T., Thamm, F., & Maier, A. (2024). Privacy-enhancing Image Sampling for the Synthesis of High-quality Anonymous Chest Radiographs. In Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2024. BVM 2024 (pp. 27-32). Erlangen, DE: Wiesbaden: Springer Vieweg.

MLA:

Packhäuser, Kai, et al. "Privacy-enhancing Image Sampling for the Synthesis of High-quality Anonymous Chest Radiographs." Proceedings of the German Conference on Medical Image Computing, BVM 2024, Erlangen Ed. Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff, Wiesbaden: Springer Vieweg, 2024. 27-32.

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