Jure Žbontar | DeepAI

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Yann LeCun
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∙ 04/14/2020

End-to-End Variational Networks for Accelerated MRI Reconstruction

The slow acquisition speed of magnetic resonance imaging (MRI) has led t...

5 Anuroop Sriram, et al. ∙

∙ 01/06/2020

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

Purpose: To advance research in the field of machine learning for MR ima...

74 Florian Knoll, et al. ∙

∙ 10/27/2019

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

Magnetic Resonance Image (MRI) acquisition is an inherently slow process...

23 Anuroop Sriram, et al. ∙

∙ 11/21/2018

fastMRI: An Open Dataset and Benchmarks for Accelerated MRI

Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measuremen...

21 Jure Žbontar, et al. ∙

∙ 11/19/2018

Simulating single-coil MRI from the responses of multiple coils

We convert the information-rich measurements of parallel and phased-arra...

0 Mark Tygert, et al. ∙

∙ 09/18/2018

Compressed sensing with a jackknife and a bootstrap

Compressed sensing proposes to reconstruct more degrees of freedom in a ...

0 Mark Tygert, et al. ∙

∙ 06/26/2016

Fast Incremental Learning for Off-Road Robot Navigation

A promising approach to autonomous driving is machine learning. In such ...

0 Artem Provodin, et al. ∙

∙ 10/20/2015

Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches

We present a method for extracting depth information from a rectified im...

0 Jure Žbontar, et al. ∙

∙ 09/15/2014

Computing the Stereo Matching Cost with a Convolutional Neural Network

We present a method for extracting depth information from a rectified im...

0 Jure Žbontar, et al. ∙

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