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arXiv · 2601.17529

FMIR, a foundation model-based Image Registration Framework for Robust Image Registration

Fengting Zhang·Yue He·Qinghao Liu·Yaonan Wang·Xiang Chen·Hang Zhang
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Abstract

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.

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Fengting Zhang, Yue He, Qinghao Liu, Yaonan Wang, Xiang Chen, Hang Zhang. 2026-01-24. FMIR, a foundation model-based Image Registration Framework for Robust Image Registration. https://arxiv.org/abs/2601.17529

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