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Found 5 papers, 4 papers with code
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.
ICON: Learning Regular Maps Through Inverse Consistency
We explore if it is possible to obtain spatial regularity using an inverse consistency loss only and elucidate what explains map regularity in such a context.
A Dimension-free Computational Upper-bound for Smooth Optimal Transport Estimation
It is well-known that plug-in statistical estimation of optimal transport suffers from the curse of dimensionality.
Statistics Theory Optimization and Control Statistics Theory 62G05
Metric Learning for Image Registration
Our approach is a radical departure from existing deep learning approaches to image registration by embedding a deep learning model in an optimization-based registration algorithm to parameterize and data-adapt the registration model itself.
Ranked #1 on
Diffeomorphic Medical Image Registration
on CUMC12
Deformable Medical Image Registration
Diffeomorphic Medical Image Registration
+2
