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uniGradICON: A Foundation Model for Medical Image Registration
We therefore propose uniGradICON, a first step toward a foundation model for registration providing 1) great performance \emph{across} multiple datasets which is not feasible for current learning-based registration methods, 2) zero-shot capabilities for new registration tasks suitable for different acquisitions, anatomical regions, and modalities compared to the training dataset, and 3) a strong initialization for finetuning on out-of-distribution registration tasks.
Inverse Consistency by Construction for Multistep Deep Registration
Inverse consistency is a desirable property for image registration.
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