28 citations · 38 across the 6 of their papers we have counts for
6 papers
Motion correction in MRI using deep learning and a novel hybrid loss function
Lei Zhang, Xiaoke Wang, Michael Rawson +6
Purpose To develop and evaluate a deep learning-based method (MC-Net) to suppress motion artifacts in brain magnetic resonance imaging (MRI). Methods MC-Net was derived from a UNet…
A Data-Driven Approach to Extract Connectivity Structures from Diffusion Tensor Imaging Data
Yu Jin, Joseph F. JaJa, Rong Chen +1
Diffusion Tensor Imaging (DTI) is an effective tool for the analysis of structural brain connectivity in normal development and in a broad range of brain disorders. However efforts…
Scalable Algorithms for Generating and Analyzing Structural Brain Networks with a Varying Number of Nodes
Yu Jin, Joseph F. JaJa, Rong Chen +1
Diffusion Magnetic Resonance Imaging (MRI) exploits the anisotropic diffusion of water molecules in the brain to enable the estimation of the brain's anatomical fiber tracts at a r…
Ergo: A Graphical Environment for Constructing Bayesian
Ingo Beinlich, Edward H. Herskovits
We describe an environment that considerably simplifies the process of generating Bayesian belief networks. The system has been implemented on readily available, inexpensive hardwa…
Kutato: An Entropy-Driven System for Construction of Probabilistic Expert Systems from Databases
Edward H. Herskovits, Gregory F. Cooper
Kutato is a system that takes as input a database of cases and produces a belief network that captures many of the dependence relations represented by those data. This system incor…
A Bayesian Method for Constructing Bayesian Belief Networks from Databases
Gregory F. Cooper, Edward H. Herskovits
This paper presents a Bayesian method for constructing Bayesian belief networks from a database of cases. Potential applications include computer-assisted hypothesis testing, autom…