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Found 5 papers, 5 papers with code
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
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.
Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
Conclusion: The challenge led to new developments in machine learning for image reconstruction, provided insight into the current state of the art in the field, and highlighted remaining hurdles for clinical adoption.
End-to-End Variational Networks for Accelerated MRI Reconstruction
The slow acquisition speed of magnetic resonance imaging (MRI) has led to the development of two complementary methods: acquiring multiple views of the anatomy simultaneously (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing methods (compressed sensing).
Ranked #1 on
MRI Reconstruction
on fastMRI Knee 4x
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community.
COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction
The first is deterioration prediction from a single image, where our model achieves an area under receiver operating characteristic curve (AUC) of 0. 742 for predicting an adverse event within 96 hours (compared to 0. 703 with supervised pretraining) and an AUC of 0. 765 for predicting oxygen requirements greater than 6 L a day at 24 hours (compared to 0. 749 with supervised pretraining).
