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Burhaneddin Yaman

Showing results (1-10 of 17) with videos related to

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IEEE Signal Processing Magazine|October 3, 2022
Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement: An overview from a signal processing perspectiveMehmet Akçakaya, Burhaneddin Yaman, Hyungjin Chung, et al.
Pharmaceutics|June 2, 2021
Application of Deep Neural Networks as a Prescreening Tool to Assign Individualized Absorption Models in Pharmacokinetic AnalysisMutaz M Jaber, Burhaneddin Yaman, Kyriakie Sarafoglou, et al.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|December 11, 2021
Compressed Sensing MRI with ℓ<sub>1</sub>-Wavelet Reconstruction Revisited Using Modern Data Science ToolsHongyi Gu, Burhaneddin Yaman, Kamil Ugurbil, et al.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|October 6, 2020
High-Fidelity Accelerated MRI Reconstruction by Scan-Specific Fine-Tuning of Physics-Based Neural NetworksSeyed Amir Hossein Hosseini, Burhaneddin Yaman, Steen Moeller, et al.
... International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. International Workshop on Computational Advances in Multi-Sensor Adaptive Processing|January 2, 2020
Locally Low-Rank Tensor Regularization for High-Resolution Quantitative Dynamic MRIBurhaneddin Yaman, Sebastian Weingärtner, Nikolaos Kargas, et al.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|September 10, 2022
Signal-Intensity Informed Multi-Coil MRI Encoding Operator for Improved Physics-Guided Deep Learning Reconstruction of Dynamic Contrast-Enhanced MRIOmer Burak Demirel, Burhaneddin Yaman, Steen Moeller, et al.
IEEE Transactions on Computational Imaging|March 25, 2020
Low-Rank Tensor Models for Improved Multi-Dimensional MRI: Application to Dynamic Cardiac <i>T</i> <sub>1</sub> MappingBurhaneddin Yaman, Sebastian Weingärtner, Nikolaos Kargas, et al.
IEEE Journal of Selected Topics in Signal Processing|March 22, 2021
Dense Recurrent Neural Networks for Accelerated MRI: History-Cognizant Unrolling of Optimization AlgorithmsSeyed Amir Hossein Hosseini, Burhaneddin Yaman, Steen Moeller, et al.
Proceedings of the National Academy of Sciences of the United States of America|August 8, 2022
Revisiting [Formula: see text]-wavelet compressed-sensing MRI in the era of deep learningHongyi Gu, Burhaneddin Yaman, Steen Moeller, et al.
Magnetic Resonance in Medicine|September 21, 2022
Signal intensity informed multi-coil encoding operator for physics-guided deep learning reconstruction of highly accelerated myocardial perfusion CMROmer Burak Demirel, Burhaneddin Yaman, Chetan Shenoy, et al.
Pageof 2

Showing results (1-10 of 17) with videos related to

Sort By:
Pageof 2
IEEE Signal Processing Magazine|October 3, 2022
Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement: An overview from a signal processing perspectiveMehmet Akçakaya, Burhaneddin Yaman, Hyungjin Chung, et al.
Pharmaceutics|June 2, 2021
Application of Deep Neural Networks as a Prescreening Tool to Assign Individualized Absorption Models in Pharmacokinetic AnalysisMutaz M Jaber, Burhaneddin Yaman, Kyriakie Sarafoglou, et al.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|December 11, 2021
Compressed Sensing MRI with ℓ<sub>1</sub>-Wavelet Reconstruction Revisited Using Modern Data Science ToolsHongyi Gu, Burhaneddin Yaman, Kamil Ugurbil, et al.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|October 6, 2020
High-Fidelity Accelerated MRI Reconstruction by Scan-Specific Fine-Tuning of Physics-Based Neural NetworksSeyed Amir Hossein Hosseini, Burhaneddin Yaman, Steen Moeller, et al.
... International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. International Workshop on Computational Advances in Multi-Sensor Adaptive Processing|January 2, 2020
Locally Low-Rank Tensor Regularization for High-Resolution Quantitative Dynamic MRIBurhaneddin Yaman, Sebastian Weingärtner, Nikolaos Kargas, et al.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|September 10, 2022
Signal-Intensity Informed Multi-Coil MRI Encoding Operator for Improved Physics-Guided Deep Learning Reconstruction of Dynamic Contrast-Enhanced MRIOmer Burak Demirel, Burhaneddin Yaman, Steen Moeller, et al.
IEEE Transactions on Computational Imaging|March 25, 2020
Low-Rank Tensor Models for Improved Multi-Dimensional MRI: Application to Dynamic Cardiac <i>T</i> <sub>1</sub> MappingBurhaneddin Yaman, Sebastian Weingärtner, Nikolaos Kargas, et al.
IEEE Journal of Selected Topics in Signal Processing|March 22, 2021
Dense Recurrent Neural Networks for Accelerated MRI: History-Cognizant Unrolling of Optimization AlgorithmsSeyed Amir Hossein Hosseini, Burhaneddin Yaman, Steen Moeller, et al.
Proceedings of the National Academy of Sciences of the United States of America|August 8, 2022
Revisiting [Formula: see text]-wavelet compressed-sensing MRI in the era of deep learningHongyi Gu, Burhaneddin Yaman, Steen Moeller, et al.
Magnetic Resonance in Medicine|September 21, 2022
Signal intensity informed multi-coil encoding operator for physics-guided deep learning reconstruction of highly accelerated myocardial perfusion CMROmer Burak Demirel, Burhaneddin Yaman, Chetan Shenoy, et al.
Pageof 2