MRI扫描中的隐私保护：基于3D掩码自编码器
Privacy Protection in MRI Scans Using 3D Masked Autoencoders
AI总结：
提出CP-MAE模型，利用3D掩码自编码器重塑面部而非移除敏感部位，实现MRI去标识化，同时提升下游任务性能，并支持高达256^3分辨率的高保真扫描合成。
摘要：
MRI扫描提供了宝贵的医学信息，然而它们也包含需要保护的敏感和个人身份信息。虽然MRI元数据易于清理，但MRI图像数据存在隐私风险，因为它包含能够渲染患者头部高度逼真3D可视化的信息，使恶意行为者可能通过交叉引用数据库来识别受试者。数据匿名化和去标识化旨在确保个人信息的隐私和机密性。传统的MRI去标识化方法从给定扫描中移除隐私敏感部分（如眼睛、鼻子等）。这以引入领域偏移为代价，可能干扰下游分析。在这项工作中，我们提出了CP-MAE，一种通过重塑面部（例如改变面部）而非移除部分来去标识化面部的模型，使用掩码自编码器。CP-MAE在下游任务性能和去标识化方面均优于所有先前方法。利用我们的方法，我们能够合成分辨率高达$256^3$的高保真扫描——相比之下，先前方法仅为$128^3$——这相当于体素数量增加了八倍。
英文摘要：
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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