Search Results for author: Yvonne W. Lui

Found 10 papers, 5 papers with code

fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Data

1 code implementation8 Sep 2021 Ruiyang Zhao, Burhaneddin Yaman, Yuxin Zhang, Russell Stewart, Austin Dixon, Florian Knoll, Zhengnan Huang, Yvonne W. Lui, Michael S. Hansen, Matthew P. Lungren

Improving speed and image quality of Magnetic Resonance Imaging (MRI) via novel reconstruction approaches remains one of the highest impact applications for deep learning in medical imaging.

MRI Reconstruction

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

1 code implementation4 Aug 2020 Farah E. Shamout, Yiqiu Shen, Nan Wu, Aakash Kaku, Jungkyu Park, Taro Makino, Stanisław Jastrzębski, Duo Wang, Ben Zhang, Siddhant Dogra, Meng Cao, Narges Razavian, David Kudlowitz, Lea Azour, William Moore, Yvonne W. Lui, Yindalon Aphinyanaphongs, Carlos Fernandez-Granda, Krzysztof J. Geras

In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time.

COVID-19 Diagnosis Decision Making +1

DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation

1 code implementation13 Nov 2019 Aakash Kaku, Chaitra V. Hegde, Jeffrey Huang, Sohae Chung, Xiuyuan Wang, Matthew Young, Alireza Radmanesh, Yvonne W. Lui, Narges Razavian

This is also the first work to include an expert reader study to assess the quality of the segmentation obtained using a deep-learning-based model.

Brain Segmentation

Training a Neural Network for Gibbs and Noise Removal in Diffusion MRI

1 code implementation10 May 2019 Matthew J. Muckley, Benjamin Ades-Aron, Antonios Papaioannou, Gregory Lemberskiy, Eddy Solomon, Yvonne W. Lui, Daniel K. Sodickson, Els Fieremans, Dmitry S. Novikov, Florian Knoll

Both machine learning methods were able to mitigate artifacts in diffusion-weighted images and diffusion parameter maps.

Image and Video Processing

MTBI Identification From Diffusion MR Images Using Bag of Adversarial Visual Features

no code implementations27 Jun 2018 Shervin Minaee, Yao Wang, Alp Aygar, Sohae Chung, Xiuyuan Wang, Yvonne W. Lui, Els Fieremans, Steven Flanagan, Joseph Rath

Unlike most of previous works, which use hand-crafted features extracted from different parts of brain for MTBI classification, we employ feature learning algorithms to learn more discriminative representation for this task.

A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI

no code implementations8 Feb 2018 Shervin Minaee, Yao Wang, Anna Choromanska, Sohae Chung, Xiuyuan Wang, Els Fieremans, Steven Flanagan, Joseph Rath, Yvonne W. Lui

Mild traumatic brain injury is a growing public health problem with an estimated incidence of over 1. 7 million people annually in US.

Identifying Mild Traumatic Brain Injury Patients From MR Images Using Bag of Visual Words

no code implementations18 Oct 2017 Shervin Minaee, Siyun Wang, Yao Wang, Sohae Chung, Xiuyuan Wang, Els Fieremans, Steven Flanagan, Joseph Rath, Yvonne W. Lui

Mild traumatic brain injury (mTBI) is a growing public health problem with an estimated incidence of one million people annually in US.

A Machine Learning Approach For Identifying Patients with Mild Traumatic Brain Injury Using Diffusion MRI Modeling

no code implementations27 Aug 2017 Shervin Minaee, Yao Wang, Sohae Chung, Xiuyuan Wang, Els Fieremans, Steven Flanagan, Joseph Rath, Yvonne W. Lui

While diffusion MRI has been extremely promising in the study of MTBI, identifying patients with recent MTBI remains a challenge.

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