2.2k citations · 5.7k across the 59 of their papers we have counts for
9 papers · 1 filter
Fast Learning-based Registration of Sparse 3D Clinical Images
Kathleen M. Lewis, Natalia S. Rost, John Guttag +1
We introduce SparseVM, a method that registers clinical-quality 3D MR scans both faster and more accurately than previously possible. Deformable alignment, or registration, of clin…
Gaussian Process Prior Variational Autoencoders
Francesco Paolo Casale, Adrian V Dalca, Luca Saglietti +2
Variational autoencoders (VAE) are a powerful and widely-used class of models to learn complex data distributions in an unsupervised fashion. One important limitation of VAEs is th…
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
Natalia Antropova, Andrew L. Beam, Brett K. Beaulieu-Jones +15
This volume represents the accepted submissions from the Machine Learning for Health (ML4H) workshop at the conference on Neural Information Processing Systems (NeurIPS) 2018, held…
Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease
Danielle F. Pace, Adrian V. Dalca, Tom Brosch +5
We propose a new iterative segmentation model which can be accurately learned from a small dataset. A common approach is to train a model to directly segment an image, requiring a…
VoxelMorph: A Learning Framework for Deformable Medical Image Registration
Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu +2
We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical image registration. Traditional registration methods optimize an objective function for each…
Medical Image Imputation from Image Collections
Adrian V. Dalca, Katherine L. Bouman, William T. Freeman +3
We present an algorithm for creating high resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing. Although large…