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[bibtex]@InProceedings{Tian_2025_CVPR, author = {Tian, Yuan and Ji, Kaiyuan and Zhang, Rongzhao and Jiang, Yankai and Li, Chunyi and Wang, Xiaosong and Zhai, Guangtao}, title = {Towards All-in-One Medical Image Re-Identification}, booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)}, month = {June}, year = {2025}, pages = {30774-30786} }
Towards All-in-One Medical Image Re-Identification
Abstract
Medical image re-identification (MedReID) is under-explored 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 modality-specific 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 multi-modal language models across 11 image datasets, demonstrating consistently superior performance.Additionally, we deploy the proposed MedReID technique to two real-world applications, i.e., history-augmented personalized diagnosis and medical privacy protection.
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