A Utility-preserving De-identification Pipeline for Cross-hospital Radiology Data Sharing

• URL: http://arxiv.org/abs/2604.07128v1
• Date: Wed, 08 Apr 2026 14:21:29 GMT
• Title: A Utility-preserving De-identification Pipeline for Cross-hospital Radiology Data Sharing
• Abstract summary: We introduce a utility-preserving de-identification pipeline for cross-hospital radiology data sharing.<n>For radiology images, we use a generative filtering mechanism that synthesise a privacy-filtered and pathology-reserved counterparts of the original images.<n>Experiments on public chest X-ray benchmarks demonstrate that our method effectively removes privacy-sensitive information while preserving diagnostically relevant pathology cues.
• Score: 26.834444576458623
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 training and cross-hospital transfer remains underexplored. In this paper, we introduce a utility-preserving de-identification pipeline (UPDP) for cross-hospital radiology data sharing. Specifically, we compile a blacklist of privacy-sensitive terms and a whitelist of pathology-related terms. For radiology images, we use a generative filtering mechanism that synthesis a privacy-filtered and pathology-reserved counterparts of the original images. These synthetic image counterparts, together with ID-filtered reports, can then be securely shared across hospitals for downstream model development and evaluation. Experiments on public chest X-ray benchmarks demonstrate that our method effectively removes privacy-sensitive information while preserving diagnostically relevant pathology cues. Models trained on the de-identified data maintain competitive diagnostic accuracy compared with those trained on the original data, while exhibiting a marked decline in identity-related accuracy, confirming effective privacy protection. In the cross-hospital setting, we further show that de-identified data can be combined with local data to yield better performance.

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