bibtext: @InProceedings{ Van_Privacy_MICCAI2024,
                   author = { Van der Goten, Lennart A. and Smith, Kevin },
                   title = { { Privacy Protection in MRI Scans Using 3D Masked Autoencoders } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15007 },
                   month = {October},
                   pages = { pending },
                }
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 on the ADNI and OASIS-3 datasets – compared to 128^3 with previous approaches – which constitutes an eight-fold increase in the number of voxels.
title: Privacy Protection in MRI Scans Using 3D Masked Autoencoders
authors: Van der Goten, Lennart A. Smith, Kevin