Tullie Murrell

Ego4D: Around the World in 3,600 Hours of Egocentric Video

Kristen Grauman, Andrew Westbury, Eugene H. Byrne, Vincent Cartillier et autres

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers from 74 worldwide locations and 9 different countries. …

us, it, gb, sa, sg, in, Rwanda, cl, bo, jp (code pays fourni par la source)

Properties of 2D MR image reconstructions with deep neural networks at high acceleration rates

Matthew J. Muckley, Tullie Murrell, Alireza Radmanesh, Florian Johannes Knoll et autres

We assess the properties of deep neural network-reconstructed brain MR images in the high acceleration regime at factors up to 100. We have three contributions: 1) metrics on model performance from 2- to 100-fold accelerations, 2) a Monte Carlo procedure for scoring …

Exploring the Acceleration Limits of Deep Learning Variational Network–based Two-dimensional Brain MRI

Alireza Radmanesh, Matthew J. Muckley, Tullie Murrell, Emma Lindsey et autres

Purpose To explore the limits of deep learning–based brain MRI reconstruction and identify useful acceleration ranges for general-purpose imaging and potential screening. Materials and Methods In this retrospective study conducted from 2019 through 2021, a model was trained for reconstruction on 5847 …

il, us, de (code pays fourni par la source)

Ego4D: Around the World in 3,000 Hours of Egocentric Video

Kristen Grauman, Andrew Westbury, Eugene H. Byrne, Zachary Chavis et autres

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of dailylife activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers from 74 worldwide locations and 9 different countries. …

us, it, gb, sa, sg, in, Rwanda, bo, jp, ru (code pays fourni par la source)

PyTorchVideo: A Deep Learning Library for Video Understanding

Haoqi Fan, Tullie Murrell, Heng Wang, Kalyan Vasudev Alwala et autres

We introduce PyTorchVideo, an open-source deep-learning library that provides a rich set of modular, efficient, and reproducible components for a variety of video understanding tasks, including classification, detection, self-supervised learning, and low-level processing. The library covers a full stack of video understanding …

PyTorchVideo

Haoqi Fan, Tullie Murrell, Heng Wang, Kalyan Vasudev Alwala et autres

We introduce PyTorchVideo, an open-source deep-learning library that provides a rich set of modular, efficient, and reproducible components for a variety of video understanding tasks, including classification, detection, self-supervised learning, and low-level processing. The library covers a full stack of video understanding …

us (code pays fourni par la source)

Ego4D: Around the World in 3,000 Hours of Egocentric Video

Kristen Grauman, Andrew Westbury, Eugene H. Byrne, Zachary Chavis et autres

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers from 74 worldwide locations and 9 different countries. …

Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability Study

Michael P. Recht, Jure Žbontar, Daniel K. Sodickson, Florian Johannes Knoll et autres

An optimized DL model allowed acceleration of knee images that performed interchangeably with standard images for detection of internal derangement of the knee. Importantly, readers preferred the quality of accelerated images to that of standard clinical images.

us, il (code pays fourni par la source)

Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge

Florian Johannes Knoll, Tullie Murrell, Anuroop Sriram, Nafissa Yakubova et autres

PURPOSE: To advance research in the field of machine learning for MR image reconstruction with an open challenge. METHODS: We provided participants with a dataset of raw k-space data from 1,594 consecutive clinical exams of the knee. The goal of the challenge …

us (code pays fourni par la source)

GrappaNet: Combining Parallel Imaging With Deep Learning for Multi-Coil MRI Reconstruction

Anuroop Sriram, Jure Žbontar, Tullie Murrell, C. Lawrence Zitnick et autres

Magnetic Resonance Image (MRI) acquisition is an inherently slow process which has spurred the development of two different acceleration methods: acquiring multiple correlated samples simultaneously (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing methods (compressed sensing). Both methods …

il (code pays fourni par la source)

PyTorchLightning/pytorch-lightning: TPU support & profiling

William Falcon, Jirka Borovec, Nic Eggert, Vadim Bereznyuk et autres

Overview This is the first joined release with pytorch-bearer, here we come... This release extends the training features by addIng Tensor Processing Unit (TPU) support, see docs. It brings together the flexibility from pytorch-bearer of extended support for user-defined callbacks, see docs. …

il, cz, us, cn, gb, in, ca, nl (code pays fourni par la source)

MRI Banding Removal via Adversarial Training

Aaron Defazio, Tullie Murrell, Michael P. Recht

MRI images reconstructed from sub-sampled Cartesian data using deep learning techniques often show a characteristic banding (sometimes described as streaking), which is particularly strong in low signal-to-noise regions of the reconstructed image. In this work, we propose the use of an adversarial …