Privacy Protection in MRI Scans Using 3D Masked Autoencoders
arxiv(2023)
摘要
MRI scans provide valuable medical information, however they also contain
sensitive and personally identifiable information that needs to be protected.
Whereas MRI metadata is easily sanitized, MRI image data is a privacy risk
because it contains information to render highly-realistic 3D visualizations of
a patient's head, enabling malicious actors to possibly identify the subject by
cross-referencing a database. Data anonymization and de-identification is
concerned with ensuring the privacy and confidentiality of individuals'
personal information. Traditional MRI de-identification methods remove
privacy-sensitive parts (e.g. eyes, nose etc.) from a given scan. This comes at
the expense of introducing a domain shift that can throw off downstream
analyses. In this work, we propose CP-MAE, a model that de-identifies the face
by remodeling it (e.g. changing the face) rather than by removing parts using
masked autoencoders. CP-MAE outperforms all previous approaches in terms of
downstream task performance as well as de-identification. With our method we
are able to synthesize high-fidelity scans of resolution up to 256^3 –
compared to 128^3 with previous approaches – which constitutes an eight-fold
increase in the number of voxels.
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