Privacy Protection in MRI Scans Using 3D Masked Autoencoders

• URL: http://arxiv.org/abs/2310.15778v3
• Date: Mon, 18 Mar 2024 13:27:01 GMT
• Title: Privacy Protection in MRI Scans Using 3D Masked Autoencoders
• Authors: Lennart Alexander Van der Goten, Kevin Smith,
• Abstract summary: Data anonymization and de-identification is concerned with ensuring the privacy and confidentiality of individuals' personal information. We propose CP-MAE, a model that de-identifies the face by remodeling it. With our method we are able to synthesize high-fidelity scans of resolution up to $2563$ -- compared to $1283$ with previous approaches.
• Score: 2.463789441707266
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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