Towards All-in-One Medical Image Re-Identification

作者：Yuan Tian, Kaiyuan Ji, Rongzhao Zhang, Yankai Jiang, Chunyi Li, Xiaosong Wang, Guangtao Zhai · 年份：2025 · DOI：10.1109/cvpr52734.2025.02866 · 被引用次数：6 · 研究领域：Brain Tumor Detection and Classification、AI in cancer detection、Medical Image Segmentation Techniques

Medical image re-identification (MedReID) is underexplored so far, despite its critical applications in personalized healthcare and privacy protection. In this paper, we introduce a thorough benchmark and a unified model for this problem. First, to handle various medical modalities, we propose a novel Continuous Modality-based Parameter Adapter (ComPA). ComPA condenses medical content into a continuous modality representation and dynamically adjusts the modality-agnostic model with modalityspecific parameters at runtime. This allows a single model to adaptively learn and process diverse modality data. Furthermore, we integrate medical priors into our model by aligning it with a bag of pre-trained medical foundation models, in terms of the differential features. Compared to single-image feature, modeling the inter-image difference better fits the re-identification problem, which involves discriminating multiple images. We evaluate the proposed model against 25 foundation models and 8 large multimodal language models across 11 image datasets, demonstrating consistently superior performance. Additionally, we deploy the proposed MedReID technique to two realworld applications, i.e., history-augmented personalized diagnosis and medical privacy protection. Codes and model is available at https://github.com/tianyuan168326/All-inOne-MedReID-Pytorch.