A Configurable Privacy-Preserving MRI Processing Workflow Using Deep Learning-Based Brain Extraction and Adaptive Anatomical Preservation
A modular framework for balancing neuroimaging utility with patient privacy via adaptive deep learning workflows.
Structural Magnetic Resonance Imaging (MRI) is widely used in neuroimaging research and clinical practice, but structural MRI volumes may retain facial and cranial anatomical information that raises privacy concerns. Existing deep learning-based brain extraction methods generally...
ArXiv CS.CV
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This research introduces a flexible framework that balances the clinical utility of MRI data with the urgent necessity of patient facial de-identification. By enabling user-controlled privacy levels, it addresses a critical bottleneck in deploying deep learning tools within sensitive medical research environments.
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