  This paper presents a client/server privacy-preserving network in the context
of multicentric medical image analysis. Our approach is based on adversarial
learning which encodes images to obfuscate the patient identity while
preserving enough information for a target task. Our novel architecture is
composed of three components: 1) an encoder network which removes
identity-specific features from input medical images, 2) a discriminator
network that attempts to identify the subject from the encoded images, 3) a
medical image analysis network which analyzes the content of the encoded images
(segmentation in our case). By simultaneously fooling the discriminator and
optimizing the medical analysis network, the encoder learns to remove
privacy-specific features while keeping those essentials for the target task.
Our approach is illustrated on the problem of segmenting brain MRI from the
large-scale Parkinson Progression Marker Initiative (PPMI) dataset. Using
longitudinal data from PPMI, we show that the discriminator learns to heavily
distort input images while allowing for highly accurate segmentation results.
