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SuperWarp: Supervised Learning and Warping on U-Net for Invariant Subvoxel-Precise Registration
Sean I. Young, Yaël Balbastre, Adrian V. Dalca +3
In recent years, learning-based image registration methods have gradually moved away from direct supervision with target warps to instead use self-supervision, with excellent resul…
Learning the Effect of Registration Hyperparameters with HyperMorph
Andrew Hoopes, Malte Hoffmann, Douglas N. Greve +3
We introduce HyperMorph, a framework that facilitates efficient hyperparameter tuning in learning-based deformable image registration. Classical registration algorithms perform an…
SynthStrip: Skull-Stripping for Any Brain Image
Andrew Hoopes, Jocelyn S. Mora, Adrian V. Dalca +2
The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as skull-stripping, is an integral component of many neuroimage analysis streams. Despite their ab…
Hyper-Convolutions via Implicit Kernels for Medical Imaging
Tianyu Ma, Alan Q. Wang, Adrian V. Dalca +1
The convolutional neural network (CNN) is one of the most commonly used architectures for computer vision tasks. The key building block of a CNN is the convolutional kernel that ag…
Computing Multiple Image Reconstructions with a Single Hypernetwork
Alan Q. Wang, Adrian V. Dalca, Mert R. Sabuncu
Deep learning based techniques achieve state-of-the-art results in a wide range of image reconstruction tasks like compressed sensing. These methods almost always have hyperparamet…
Supervision by Denoising for Medical Image Segmentation
Sean I. Young, Adrian V. Dalca, Enzo Ferrante +4
Learning-based image reconstruction models, such as those based on the U-Net, require a large set of labeled images if good generalization is to be guaranteed. In some imaging doma…