Semantics versus Identity: A Divide-and-Conquer Approach towards Adjustable Medical Image De-Identification
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908471000039424 |
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| author | Tian, Yuan Wang, Shuo Zhang, Rongzhao Chen, Zijian Jiang, Yankai Li, Chunyi Zhu, Xiangyang Yan, Fang Hu, Qiang Wang, XiaoSong Zhai, Guangtao |
| author_facet | Tian, Yuan Wang, Shuo Zhang, Rongzhao Chen, Zijian Jiang, Yankai Li, Chunyi Zhu, Xiangyang Yan, Fang Hu, Qiang Wang, XiaoSong Zhai, Guangtao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21703 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Semantics versus Identity: A Divide-and-Conquer Approach towards Adjustable Medical Image De-Identification Tian, Yuan Wang, Shuo Zhang, Rongzhao Chen, Zijian Jiang, Yankai Li, Chunyi Zhu, Xiangyang Yan, Fang Hu, Qiang Wang, XiaoSong Zhai, Guangtao Computer Vision and Pattern Recognition 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. |
| title | Semantics versus Identity: A Divide-and-Conquer Approach towards Adjustable Medical Image De-Identification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.21703 |