AI RESEARCH
A Utility-preserving De-identification Pipeline for Cross-hospital Radiology Data Sharing
arXiv CS.CV
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ArXi:2604.07128v1 Announce Type: new Large-scale radiology data are critical for developing robust medical AI systems. However, sharing such data across hospitals remains heavily constrained by privacy concerns. Existing de-identification research in radiology mainly focus on removing identifiable information to enable compliant data release. Yet whether de-identified radiology data can still preserve sufficient utility for large-scale vision-language model