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Privacy Preserving for Medical Image Analysis via Non-Linear Deformation Proxy
Results show that the segmentation accuracy of our method is similar to a system trained on non-encoded images, while considerably reducing the ability to recover subject identity.
Privacy-Net: An Adversarial Approach for Identity-Obfuscated Segmentation of Medical Images
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).
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