FMIR, a foundation model-based Image Registration Framework for Robust Image Registration
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914292812480512 |
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| author | Zhang, Fengting He, Yue Liu, Qinghao Wang, Yaonan Chen, Xiang Zhang, Hang |
| author_facet | Zhang, Fengting He, Yue Liu, Qinghao Wang, Yaonan Chen, Xiang Zhang, Hang |
| contents | Deep learning has revolutionized medical image registration by achieving unprecedented speeds, yet its clinical application is hindered by a limited ability to generalize beyond the training domain, a critical weakness given the typically small scale of medical datasets. In this paper, we introduce FMIR, a foundation model-based registration framework that overcomes this limitation.Combining a foundation model-based feature encoder for extracting anatomical structures with a general registration head, and trained with a channel regularization strategy on just a single dataset, FMIR achieves state-of-the-art(SOTA) in-domain performance while maintaining robust registration on out-of-domain images.Our approach demonstrates a viable path toward building generalizable medical imaging foundation models with limited resources. The code is available at https://github.com/Monday0328/FMIR.git. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17529 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FMIR, a foundation model-based Image Registration Framework for Robust Image Registration Zhang, Fengting He, Yue Liu, Qinghao Wang, Yaonan Chen, Xiang Zhang, Hang Computer Vision and Pattern Recognition Deep learning has revolutionized medical image registration by achieving unprecedented speeds, yet its clinical application is hindered by a limited ability to generalize beyond the training domain, a critical weakness given the typically small scale of medical datasets. In this paper, we introduce FMIR, a foundation model-based registration framework that overcomes this limitation.Combining a foundation model-based feature encoder for extracting anatomical structures with a general registration head, and trained with a channel regularization strategy on just a single dataset, FMIR achieves state-of-the-art(SOTA) in-domain performance while maintaining robust registration on out-of-domain images.Our approach demonstrates a viable path toward building generalizable medical imaging foundation models with limited resources. The code is available at https://github.com/Monday0328/FMIR.git. |
| title | FMIR, a foundation model-based Image Registration Framework for Robust Image Registration |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.17529 |