fastMRI: An Open Dataset and Benchmarks for Accelerated MRI

11 code implementations • 21 Nov 2018 • Jure Zbontar, Florian Knoll, Anuroop Sriram, Tullie Murrell, Zhengnan Huang, Matthew J. Muckley, Aaron Defazio, Ruben Stern, Patricia Johnson, Mary Bruno, Marc Parente, Krzysztof J. Geras, Joe Katsnelson, Hersh Chandarana, Zizhao Zhang, Michal Drozdzal, Adriana Romero, Michael Rabbat, Pascal Vincent, Nafissa Yakubova, James Pinkerton, Duo Wang, Erich Owens, C. Lawrence Zitnick, Michael P. Recht, Daniel K. Sodickson, Yvonne W. Lui

Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive.

BIG-bench Machine Learning Image Reconstruction

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

1 code implementation • 8 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

DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation

1 code implementation • 13 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 Segmentation

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

1 code implementation • 4 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

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

1 code implementation • 10 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

A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI

no code implementations • 8 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 implementations • 18 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.

feature selection

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

no code implementations • 27 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.

BIG-bench Machine Learning

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

no code implementations • 27 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.

Identification of relevant diffusion MRI metrics impacting cognitive functions using a novel feature selection method

no code implementations • 10 Aug 2019 • Tongda Xu, Xiyan Cai, Yao Wang, Xiuyuan Wang, Sohae Chung, Els Fieremans, Joseph Rath, Steven Flanagan, Yvonne W. Lui

Mild Traumatic Brain Injury (mTBI) is a significant public health problem.

feature selection