Generative Medical Image Anonymization Based on Latent Code Projection and Optimization
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Abstract (arXiv)
Medical image anonymization aims to protect patient privacy by removing identifying information, while preserving the data utility to solve downstream tasks. In this paper, we address the medical image anonymization problem with a two-stage solution: latent code projection and optimization. In the projection stage, we design a streamlined encoder to project input images into a latent space and propose a co-training scheme to enhance the projection process. In the optimization stage, we refine the latent code using two deep loss functions designed to address the trade-off between identity protection and data utility dedicated to medical images. Through a comprehensive set of qualitative and quantitative experiments, we showcase the effectiveness of our approach on the MIMIC-CXR chest X-ray dataset by generating anonymized synthetic images that can serve as training set for detecting lung pathologies. Source codes are available at https://github.com/Huiyu-Li/GMIA.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 8 references that are in the map. First posted on arXiv 15 Jan 2025.
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6 of 8 references.
References · 6
- 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
- 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
- 2023Attribute-preserving Face Dataset Anonymization via Latent Code Optimizationcited by 43
- 2021Deep Learning-based Patient Re-identification Is able to Exploit the Biometric Nature of Medical Chest X-ray Datacited by 38
- 2023A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applicationscited by 5
- 2023k-SALSA: k-anonymous synthetic averaging of retinal images via local style alignmentcited by 3
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Citing papers per year: 2025: 1.
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