  Privacy protection of medical image data is challenging. Even if metadata is
removed, brain scans are vulnerable to attacks that match renderings of the
face to facial image databases. Solutions have been developed to de-identify
diagnostic scans by obfuscating or removing parts of the face. However, these
solutions either fail to reliably hide the patient's identity or are so
aggressive that they impair further analyses. We propose a new class of
de-identification techniques that, instead of removing facial features,
remodels them. Our solution relies on a conditional multi-scale GAN
architecture. It takes a patient's MRI scan as input and generates a 3D volume
conditioned on the patient's brain, which is preserved exactly, but where the
face has been de-identified through remodeling. We demonstrate that our
approach preserves privacy far better than existing techniques, without
compromising downstream medical analyses. Analyses were run on the OASIS-3 and
ADNI corpora.
