Conditional De-Identification of 3D Magnetic Resonance Images
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 34 references that are in the map. First posted on arXiv 18 Oct 2021.
Builds on
11 of 34 references.
References · 11
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
- 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
- 2015SSD: Single Shot MultiBox Detectorcited by 3,130
- 2017Improved Training of Wasserstein GANscited by 2,235
- 2018The relativistic discriminator: a key element missing from standard GANcited by 238
- 2018Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networkscited by 147
- 2019DeepPrivacy: A Generative Adversarial Network for Face Anonymizationcited by 108
- 2019MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networkscited by 14
- 2020AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weightscited by 10
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Citing papers per year: 2023: 2.
2 papers.