{"ID":2890352,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.21703","arxiv_id":"2507.21703","title":"Semantics versus Identity: A Divide-and-Conquer Approach towards Adjustable Medical Image De-Identification","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.","short_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 differen...","url_abs":"https://arxiv.org/abs/2507.21703","url_pdf":"https://arxiv.org/pdf/2507.21703v1","authors":"[\"Yuan Tian\",\"Shuo Wang\",\"Rongzhao Zhang\",\"Zijian Chen\",\"Yankai Jiang\",\"Chunyi Li\",\"Xiangyang Zhu\",\"Fang Yan\",\"Qiang Hu\",\"XiaoSong Wang\",\"Guangtao Zhai\"]","published":"2025-07-25T06:59:05Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}