Abstract
Retina images are considered to be important biomarkers and have been used as clinical diagnostic tools to detect multiple diseases. We examine multiple techniques for de-identifying retina images while maintaining their clinical ability for detecting diabetic retinopathy (DR), using gender as a proxy for identifiability. We apply two differential privacy algorithms, Snow and VS-Snow, on the entire image (globally) and on blood vessels only (locally) to obfuscate important image features that can predict a patient’s sex. We evaluate the level of privacy and retained clinical predictive power of these de-identified images by using attacking gender classifier models and downstream disease classifiers. We show empirically that our proposed VS-Snow framework achieves strong privacy while preserving a meaningful clinical predictive power across different patient populations.
C. Wu and X. Yang—These authors contributed equally.
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27 November 2023
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Wu, C. et al. (2023). De-identification and Obfuscation of Gender Attributes from Retinal Scans. In: Wesarg, S., et al. Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging. CLIP EPIMI FAIMI 2023 2023 2023. Lecture Notes in Computer Science, vol 14242. Springer, Cham. https://doi.org/10.1007/978-3-031-45249-9_9
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