Semantics versus Identity: A Divide-and-Conquer Approach towards Adjustable Medical Image De-Identification

• URL: http://arxiv.org/abs/2507.21703v1
• Date: Fri, 25 Jul 2025 06:59:05 GMT
• Title: Semantics versus Identity: A Divide-and-Conquer Approach towards Adjustable Medical Image De-Identification
• Authors: Yuan Tian, Shuo Wang, Rongzhao Zhang, Zijian Chen, Yankai Jiang, Chunyi Li, Xiangyang Zhu, Fang Yan, Qiang Hu, XiaoSong Wang, Guangtao Zhai,
• Abstract summary: Medical imaging has significantly advanced computer-aided diagnosis, yet its re-identification (ReID) risks raise critical privacy concerns.<n>We propose a divide-and-conquer framework comprising two steps: (1) Identity-Blocking, which blocks varying proportions of identity-related regions, to achieve different privacy levels; and (2) Medical-Semantics-Compensation, which leverages pre-trained Medical Foundation Models (MFMs) to extract medical semantic features to compensate the blocked regions.
• Score: 40.752955342198824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical imaging has significantly advanced computer-aided diagnosis, yet its re-identification (ReID) risks raise critical privacy concerns, calling for de-identification (DeID) techniques. Unfortunately, existing DeID methods neither particularly preserve medical semantics, nor are flexibly adjustable towards different privacy levels. To address these issues, we propose a divide-and-conquer framework comprising two steps: (1) Identity-Blocking, which blocks varying proportions of identity-related regions, to achieve different privacy levels; and (2) Medical-Semantics-Compensation, which leverages pre-trained Medical Foundation Models (MFMs) to extract medical semantic features to compensate the blocked regions. Moreover, recognizing that features from MFMs may still contain residual identity information, we introduce a Minimum Description Length principle-based feature decoupling strategy, to effectively decouple and discard such identity components. Extensive evaluations against existing approaches across seven datasets and three downstream tasks, demonstrates our state-of-the-art performance.

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