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Anuroop Sriram

Showing results (1-10 of 10) with videos related to

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Radiology. Artificial Intelligence|December 16, 2022
Exploring the Acceleration Limits of Deep Learning Variational Network-based Two-dimensional Brain MRIAlireza Radmanesh, Matthew J Muckley, Tullie Murrell, et al.
Arxiv|January 20, 2021
COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image PredictionAnuroop Sriram, Matthew Muckley, Koustuv Sinha, et al.
ACS Central Science|May 27, 2024
The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air CaptureAnuroop Sriram, Sihoon Choi, Xiaohan Yu, et al.
Magnetic Resonance in Medicine|June 8, 2020
Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challengeFlorian Knoll, Tullie Murrell, Anuroop Sriram, et al.
BMC Public Health|October 10, 2013
FRED (a Framework for Reconstructing Epidemic Dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populationsJohn J Grefenstette, Shawn T Brown, Roni Rosenfeld, et al.
Radiology|January 17, 2023
Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRIPatricia M Johnson, Dana J Lin, Jure Zbontar, et al.
Scientific Data|February 4, 2026
Open Molecular Crystals 2025 (OMC25) dataset and modelsVahe Gharakhanyan, Luis Barroso-Luque, Yi Yang, et al.
IEEE Transactions on Medical Imaging|April 30, 2021
Results of the 2020 fastMRI Challenge for Machine Learning MR Image ReconstructionMatthew J Muckley, Bruno Riemenschneider, Alireza Radmanesh, et al.
AJR. American Journal of Roentgenology|August 7, 2020
Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability StudyMichael P Recht, Jure Zbontar, Daniel K Sodickson, et al.
Radiology. Artificial Intelligence|February 21, 2020
fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine LearningFlorian Knoll, Jure Zbontar, Anuroop Sriram, et al.
Pageof 1

Showing results (1-10 of 10) with videos related to

Sort By:
Pageof 1
Radiology. Artificial Intelligence|December 16, 2022
Exploring the Acceleration Limits of Deep Learning Variational Network-based Two-dimensional Brain MRIAlireza Radmanesh, Matthew J Muckley, Tullie Murrell, et al.
Arxiv|January 20, 2021
COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image PredictionAnuroop Sriram, Matthew Muckley, Koustuv Sinha, et al.
ACS Central Science|May 27, 2024
The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air CaptureAnuroop Sriram, Sihoon Choi, Xiaohan Yu, et al.
Magnetic Resonance in Medicine|June 8, 2020
Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challengeFlorian Knoll, Tullie Murrell, Anuroop Sriram, et al.
BMC Public Health|October 10, 2013
FRED (a Framework for Reconstructing Epidemic Dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populationsJohn J Grefenstette, Shawn T Brown, Roni Rosenfeld, et al.
Radiology|January 17, 2023
Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRIPatricia M Johnson, Dana J Lin, Jure Zbontar, et al.
Scientific Data|February 4, 2026
Open Molecular Crystals 2025 (OMC25) dataset and modelsVahe Gharakhanyan, Luis Barroso-Luque, Yi Yang, et al.
IEEE Transactions on Medical Imaging|April 30, 2021
Results of the 2020 fastMRI Challenge for Machine Learning MR Image ReconstructionMatthew J Muckley, Bruno Riemenschneider, Alireza Radmanesh, et al.
AJR. American Journal of Roentgenology|August 7, 2020
Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability StudyMichael P Recht, Jure Zbontar, Daniel K Sodickson, et al.
Radiology. Artificial Intelligence|February 21, 2020
fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine LearningFlorian Knoll, Jure Zbontar, Anuroop Sriram, et al.
Pageof 1