Communication Dans Un Congrès Année : 2025

Generative medical image anonymization based on latent code projection and optimization

Résumé

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 cotraining 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
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Dates et versions

hal-04913904 , version 1 (27-01-2025)

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  • HAL Id : hal-04913904 , version 1

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Huiyu Li, Nicholas Ayache, Hervé Delingette. Generative medical image anonymization based on latent code projection and optimization. IEEE International Symposium on Biomedical Imaging (ISBI 2025), Apr 2025, Houston (Texas), United States. ⟨hal-04913904⟩
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