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This paper presents a client/server privacy-preserving network in the contextof multicentric medical image analysis. Our approach is based on adversariallearning which encodes images to obfuscate the patient identity whilepreserving enough information for a target task. Our novel architecture iscomposed of three components: 1) an encoder network which removesidentity-specific features from input medical images, 2) a discriminatornetwork that attempts to identify the subject from the encoded images, 3) amedical image analysis network which analyzes the content of the encoded images(segmentation in our case). By simultaneously fooling the discriminator andoptimizing the medical analysis network, the encoder learns to removeprivacy-specific features while keeping those essentials for the target task.Our approach is illustrated on the problem of segmenting brain MRI from thelarge-scale Parkinson Progression Marker Initiative (PPMI) dataset. Usinglongitudinal data from PPMI, we show that the discriminator learns to heavilydistort input images while allowing for highly accurate segmentation results.

Privacy-Net: An Adversarial Approach for Identity-Obfuscated Segmentation of Medical Images