用基础模型提升医学影像配准泛化能力，小数据也能达顶尖效果
FMIR, a foundation model-based Image Registration Framework for Robust Image Registration
- 基于基础模型提取解剖结构特征，配合通用配准头
- 仅在单一数据集训练即达当前最佳域内性能
- 适合资源有限却需强泛化能力的医学影像研究者
深度学习虽极大提升了医学图像配准速度，但临床应用受限于泛化能力弱——尤其在医疗数据规模小的情况下。本文提出FMIR，一种基于基础模型的配准框架，结合基础模型特征编码器与通用配准头，并通过通道正则化策略在单个数据集上训练。该方法在域内表现达到当前最优（SOTA），同时对域外图像仍保持鲁棒性，为资源有限条件下构建可泛化的医学影像基础模型提供了可行路径。代码已开源。
原文摘要 · Abstract (English)
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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