Most state-of-the-art techniques for medical image segmentation rely on
...
Machine learning (ML) in healthcare presents numerous opportunities for
...
We present KeyMorph, a deep learning-based image registration framework ...
While deep learning models have become the predominant method for medica...
Longitudinal studies, where a series of images from the same set of
indi...
Head MRI pre-processing involves converting raw images to an
intensity-n...
In this paper, we empirically analyze a simple, non-learnable, and
nonpa...
Quantitative susceptibility mapping (QSM) involves acquisition and
recon...
Recent advances in deep learning have been driven by large-scale paramet...
Semantic segmentation is an important task in computer vision that is of...
Deep learning based techniques achieve state-of-the-art results in a wid...
The convolutional neural network (CNN) is one of the most commonly used
...
Monte Carlo (MC) dropout is a simple and efficient ensembling method tha...
The convolution operation is a central building block of neural network
...
Compressed sensing fluorescence microscopy (CS-FM) proposes a scheme whe...
Functional MRI (fMRI) is a powerful technique that has allowed us to
cha...
Reconstructing under-sampled k-space measurements in Compressed Sensing ...
We present a semi-parametric generative model for predicting anatomy of ...
Visual perception is critically influenced by the focus of attention. Du...
Many fields view stochasticity as a way to gain computational efficiency...
Resting-state functional MRI (rsfMRI) yields functional connectomes that...
Calibrated estimates of uncertainty are critical for many real-world com...
Compressed Sensing MRI (CS-MRI) has shown promise in reconstructing
unde...
The increasing popularity of naturalistic paradigms in fMRI (such as mov...
It has been recently demonstrated that multi-generational self-distillat...
Deep neural networks are powerful tools for biomedical image segmentatio...
Deep neural networks yield promising results in a wide range of computer...
Brain lesion volume measured on T2 weighted MRI images is a clinically
i...
Resting-state functional MRI (rs-fMRI) is a rich imaging modality that
c...
We tackle biomedical image segmentation in the scenario of only a few la...
We develop a learning framework for building deformable templates, which...
In compressed sensing MRI, k-space measurements are under-sampled to ach...
In classification applications, we often want probabilistic predictions ...
Probabilistic atlas priors have been commonly used to derive adaptive an...
Classical deformable registration techniques achieve impressive results ...
A wide range of systems exhibit high dimensional incomplete data. Accura...
We consider the problem of segmenting a biomedical image into anatomical...
Acquisition of Magnetic Resonance Imaging (MRI) scans can be accelerated...
Machine learning techniques have gained prominence for the analysis of
r...
We propose a novel machine learning strategy for studying neuroanatomica...
We present VoxelMorph, a fast, unsupervised, learning-based algorithm fo...
The specificty and sensitivity of resting state functional MRI (rs-fMRI)...
We present an algorithm for creating high resolution anatomically plausi...
Deep neural networks (DNNs) have achieved tremendous success in a variet...
Traditional deformable registration techniques achieve impressive result...
We propose a 3D convolutional neural network to simultaneously segment a...
In this work, we consider the problem of predicting the course of a
prog...
We present an efficient learning-based algorithm for deformable, pairwis...
The health and function of tissue rely on its vasculature network to pro...