De-identification and Obfuscation of Gender Attributes from Retinal Scans
Authors: 
Chenwei Wu, Xiyu Yang, Emil Ghitman Gilkes, Hanwen Cui, Jiheon Choi, Na Sun, Ziqian Liao, Bo Fan, Mauricio Santillana, Leo Celi, Paolo Silva, Luis NakayamaAuthors Info & Claims
Pages 91 - 101
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.
References
[1]
Ataş I Human gender prediction based on deep transfer learning from panoramic dental radiograph images Traitement du Signal 2022 39 5 1585
[2]
Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)
[3]
Goldberger A et al. Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals Circulation [Online] 2000 101 23 E215-E220
[4]
Gulshan V et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs JAMA 2016 316 22 2402-2410
[5]
He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., Li, M.: Bag of tricks for image classification with convolutional neural networks (2018)
[6]
John, B., Liu, A., Xia, L., Koppal, S., Jain, E.: Let it snow: adding pixel noise to protect the user’s identity. In: ACM Symposium on Eye Tracking Research and Applications. ETRA 2020 Adjunct, New York, NY, USA. Association for Computing Machinery (2020).
[7]
Kim YD et al. Effects of hypertension, diabetes, and smoking on age and sex prediction from retinal fundus images Sci. Rep. 2020 10 4623
[8]
Korot E et al. Predicting sex from retinal fundus photographs using automated deep learning Sci. Rep. 2021 11 10286
[9]
Larrazabal AJ, Nieto N, Peterson V, Milone DH, and Ferrante E Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis Proc. Natl. Acad. Sci. 2020 117 23 12592-12594
[10]
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection (2018)
[11]
Liu W et al. Full-resolution network and dual-threshold iteration for retinal vessel and coronary angiograph segmentation IEEE J. Biomed. Health Inform. 2022 26 9 4623-4634
[12]
Liu X et al. Deep learning to detect oct-derived diabetic macular edema from color retinal photographs: a multicenter validation study Ophthalmol. Retina 2022 6 5 398-410
[13]
Munk MR, Kurmann T, Márquez-Neila P, Zinkernagel MS, Wolf S, and Sznitman R Assessment of patient specific information in the wild on fundus photography and optical coherence tomography Sci. Rep. 2021 11 8621
[14]
Nakayama, L.F., et al.: A Brazilian multilabel ophthalmological dataset (brset) (2023).
[15]
Poplin R et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning Nat. Biomed. Eng. 2018 2 158-164
[16]
Ruamviboonsuk P et al. Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program NPJ Dig. Med. 2019 2 25
[17]
Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR (2019)
[18]
Ting DSW et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes JAMA 2017 318 22 2211-2223
[19]
Yala, A., et al.: Syfer: neural obfuscation for private data release. arXiv preprint arXiv:2201.12406 (2022)
[20]
Yu, Y., Lin, H., Meng, J., Wei, X., Guo, H., Zhao, Z.: Deep transfer learning for modality classification of medical images. Information 8(3) (2017)., https://www.mdpi.com/2078-2489/8/3/91
Recommendations
Impact of retinal vascular tortuosity on retinal circulation
The retinal microvasculature is a window to the systemic circulation. Systemic diseases, like diabetes and hypertension, are linked to retinal microvascular structure changes (as width, tortuosity, and branching angle). The latter results in a ...
Computational analysis of blood flow in the retinal arteries and veins using fundus image
The retina is the only tissue in which blood vessels can be visualized non-invasively in vivo. Thus, the study of the retinal hemodynamic has special interest for both physiological and pathological conditions. The aim of this study has been to develop ...
Automated Identification of Diabetic Retinopathy Stages Using Digital Fundus Images
Diabetic retinopathy (DR) is caused by damage to the small blood vessels of the retina in the posterior part of the eye of the diabetic patient. The main stages of diabetic retinopathy are non-proliferate diabetes retinopathy (NPDR) and proliferate ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Oct 2023
327 pages
ISBN:978-3-031-45248-2
DOI:10.1007/978-3-031-45249-9
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 12 October 2023
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 12 Jan 2025