23 citations · 33 across the 6 of their papers we have counts for
7 papers
DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation
Aakash Kaku, Chaitra V. Hegde, Jeffrey Huang +6
Quantitative, volumetric analysis of Magnetic Resonance Imaging (MRI) is a fundamental way researchers study the brain in a host of neurological conditions including normal maturat…
Identification of relevant diffusion MRI metrics impacting cognitive functions using a novel feature selection method
Tongda Xu, Xiyan Cai, Yao Wang +6
Mild Traumatic Brain Injury (mTBI) is a significant public health problem. The most troubling symptoms after mTBI are cognitive complaints. Studies show measurable differences betw…
MTBI Identification From Diffusion MR Images Using Bag of Adversarial Visual Features
Shervin Minaee, Yao Wang, Alp Aygar +6
In this work, we propose bag of adversarial features (BAF) for identifying mild traumatic brain injury (MTBI) patients from their diffusion magnetic resonance images (MRI) (obtaine…
Low Rank plus Sparse Decomposition of ODFs for Improved Detection of Group-level Differences and Variable Correlations in White Matter
Steven H. Baete, Jingyun Chen, Ying-Chia Lin +3
A novel approach is presented for group statistical analysis of diffusion weighted MRI datasets through voxelwise Orientation Distribution Functions (ODF). Recent advances in MRI a…
A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI
Shervin Minaee, Yao Wang, Anna Choromanska +6
Mild traumatic brain injury is a growing public health problem with an estimated incidence of over 1.7 million people annually in US. Diagnosis is based on clinical history and sym…
Identifying Mild Traumatic Brain Injury Patients From MR Images Using Bag of Visual Words
Shervin Minaee, Siyun Wang, Yao Wang +6
Mild traumatic brain injury (mTBI) is a growing public health problem with an estimated incidence of one million people annually in US. Neurocognitive tests are used to both assess…