Machine learning provides a valuable tool for analyzing high-dimensional...
Volumetric reconstruction of fetal brains from multiple stacks of MR sli...
Adoption of machine learning models in healthcare requires end users' tr...
We demonstrate an object tracking method for 3D images with fixed
comput...
We propose and demonstrate a representation learning approach by maximiz...
We show that for a wide class of harmonization/domain-invariance schemes...
Machine learning models are commonly trained end-to-end and in a supervi...
Estimating mutual information between continuous random variables is oft...
Pooled imaging data from multiple sources is subject to bias from each
s...
Supervised machine learning models often associate irrelevant nuisance
f...
We propose a novel approach to achieving invariance for deep neural netw...
Estimating the covariance structure of multivariate time series is a
fun...
Compression is at the heart of effective representation learning. Howeve...
Pooled imaging data from multiple sources is subject to variation betwee...
We present two related methods for deriving connectivity-based brain atl...
In the present work, we use information theory to understand the empiric...
Representations of data that are invariant to changes in specified nuisa...
There is no consensus on how to construct structural brain networks from...
One of the primary objectives of human brain mapping is the division of ...
In the present work we demonstrate the use of a parcellation free
connec...