One little-explored frontier of image generation and editing is the task...
Neural network prediction probabilities and accuracy are often only
weak...
Star-convex shapes arise across bio-microscopy and radiology in the form...
The human thalamus is a highly connected subcortical grey-matter structu...
Self-supervised representation learning on image-text data facilitates
c...
Motion artifacts are a pervasive problem in MRI, leading to misdiagnosis...
Image-text multimodal representation learning aligns data across modalit...
The statistical supervised learning framework assumes an input-output se...
The use of digitally reconstructed radiographs (DRRs) to solve inverse
p...
Automated analysis of chest radiography using deep learning has tremendo...
Blood oxygen level dependent (BOLD) MRI with maternal hyperoxia can asse...
Volumetric reconstruction of fetal brains from multiple stacks of MR sli...
Implicit neural representations (INRs) have become fast, lightweight too...
Training a fully convolutional network for semantic segmentation typical...
Although shape correspondence is a central problem in geometry processin...
Despite the superior performance of Deep Learning (DL) on numerous
segme...
We present a volumetric mesh-based algorithm for parameterizing the plac...
Adoption of machine learning models in healthcare requires end users' tr...
Image synthesis via Generative Adversarial Networks (GANs) of
three-dime...
Fetal motion is unpredictable and rapid on the scale of conventional MR ...
Cardiovascular diseases are the leading cause of death and require a spe...
BrainPainter is a software for the 3D visualization of human brain
struc...
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...
Most existing algorithms for automatic 3D morphometry of human brain MRI...
Machine learning models are commonly trained end-to-end and in a supervi...
Ensembling is now recognized as an effective approach for increasing the...
We present a semi-parametric generative model for predicting anatomy of ...
Estimating mutual information between continuous random variables is oft...
We propose and demonstrate a novel machine learning algorithm that asses...
Background: Clinical management decisions for acutely decompensated CHF
...
Fetal MRI is heavily constrained by unpredictable and substantial fetal
...
Fetal brain MRI is useful for diagnosing brain abnormalities but is
chal...
The history of computer science and brain sciences are intertwined. In h...
We present the findings of "The Alzheimer's Disease Prediction Of
Longit...
The TADPOLE Challenge compares the performance of algorithms at predicti...
We propose and demonstrate a joint model of anatomical shapes, image fea...
The performance and diagnostic utility of magnetic resonance imaging (MR...
We present BrainPainter, a software that automatically generates images ...
Probabilistic atlas priors have been commonly used to derive adaptive an...
We present a volumetric mesh-based algorithm for flattening the placenta...
Segmentation of structural and diffusion MRI (sMRI/dMRI) is usually perf...
We present a robust method to correct for motion in volumetric in-utero ...
We propose and demonstrate machine learning algorithms to assess the sev...
We propose a new iterative segmentation model which can be accurately le...
We present an algorithm for creating high resolution anatomically plausi...
We introduce an approach for image segmentation based on sparse
correspo...
A reliable Ultrasound (US)-to-US registration method to compensate for b...
Estimating the uncertainty in image registration is an area of current
r...