1 citations · 4 across the 10 of their papers we have counts for
6 papers · 1 filter
Investigating Demographic Bias in Brain MRI Segmentation: A Comparative Study of Deep-Learning and Non-Deep-Learning Methods
Ghazal Danaee, Marc Niethammer, Jarrett Rushmore +1
Deep-learning-based segmentation algorithms have substantially advanced the field of medical image analysis, particularly in structural delineations in MRIs. However, an important…
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…
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…
SAME++: A Self-supervised Anatomical eMbeddings Enhanced medical image registration framework using stable sampling and regularized transformation
Lin Tian, Zi Li, Fengze Liu +7
Image registration is a fundamental medical image analysis task. Ideally, registration should focus on aligning semantically corresponding voxels, i.e., the same anatomical locatio…
: 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…