2 citations · 4 across the 7 of their papers we have counts for
8 papers
Guiding Registration with Emergent Similarity from Pre-Trained Diffusion Models
Nurislam Tursynbek, Hastings Greer, Basar Demir +1
Diffusion models, while trained for image generation, have emerged as powerful foundational feature extractors for downstream tasks. We find that off-the-shelf diffusion models, tr…
multiGradICON: A Foundation Model for Multimodal Medical Image Registration
Basar Demir, Lin Tian, Thomas Hastings Greer +7
Modern medical image registration approaches predict deformations using deep networks. These approaches achieve state-of-the-art (SOTA) registration accuracy and are generally fast…
CARL: A Framework for Equivariant Image Registration
Hastings Greer, Lin Tian, Francois-Xavier Vialard +3
Image registration estimates spatial correspondences between a pair of images. These estimates are typically obtained via numerical optimization or regression by a deep network. A…
uniGradICON: A Foundation Model for Medical Image Registration
Lin Tian, Hastings Greer, Roland Kwitt +5
Conventional medical image registration approaches directly optimize over the parameters of a transformation model. These approaches have been highly successful and are used generi…
: Neural Deformation Fields for Approximately Diffeomorphic Medical Image Registration
Lin Tian, Hastings Greer, Raúl San José Estépar +2
This work proposes NePhi, a generalizable neural deformation model which results in approximately diffeomorphic transformations. In contrast to the predominant voxel-based transfor…
Inverse Consistency by Construction for Multistep Deep Registration
Hastings Greer, Lin Tian, Francois-Xavier Vialard +5
Inverse consistency is a desirable property for image registration. We propose a simple technique to make a neural registration network inverse consistent by construction, as a con…
: Approximate Diffeomorphisms via Gradient Inverse Consistency
Lin Tian, Hastings Greer, François-Xavier Vialard +6
We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration. Contrary to optimization-based registration tec…
ICON: Learning Regular Maps Through Inverse Consistency
Hastings Greer, Roland Kwitt, Francois-Xavier Vialard +1
Learning maps between data samples is fundamental. Applications range from representation learning, image translation and generative modeling, to the estimation of spatial deformat…