From Redaction to Restoration: Deep Learning for Medical Image Anonymization and Reconstruction

ArXi:2604.11376v1 Announce Type: cross Removing patient-specific information from medical images is crucial to enable sharing and open science without compromising patient identities. However, many methods currently used for deidentification have negative effects on downstream image analysis tasks because of removal of relevant but non-identifiable information. This work presents an end-to-end deep learning framework for transforming raw clinical image volumes into de-identified, analysis-ready datasets without compromising downstream utility.