| Self‐supervised learning of physics‐guided reconstruction neural networks without fully sampled reference data B Yaman, SAH Hosseini, S Moeller, J Ellermann, K Uğurbil, M Akçakaya Magnetic resonance in medicine 84 (6), 3172-3191, 2020 | 312 | 2020 |
| Dense recurrent neural networks for accelerated mri: History-cognizant unrolling of optimization algorithms SAH Hosseini, B Yaman, S Moeller, M Hong, M Akçakaya IEEE Journal of Selected Topics in Signal Processing 14 (6), 1280-1291, 2020 | 112 | 2020 |
| Self-supervised physics-based deep learning MRI reconstruction without fully-sampled data B Yaman, SAH Hosseini, S Moeller, J Ellermann, K Uğurbil, M Akçakaya 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 921-925, 2020 | 97 | 2020 |
| Unsupervised deep learning methods for biological image reconstruction and enhancement: An overview from a signal processing perspective M Akçakaya, B Yaman, H Chung, JC Ye IEEE Signal Processing Magazine 39 (2), 28-44, 2022 | 81 | 2022 |
| Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging K Hammernik, T Küstner, B Yaman, Z Huang, D Rueckert, F Knoll, ... IEEE signal processing magazine 40 (1), 98-114, 2023 | 79* | 2023 |
| Zero-shot self-supervised learning for MRI reconstruction B Yaman, SAH Hosseini, M Akçakaya International Conference on Learning Representations (ICLR), 2022 | 79 | 2022 |
| fastMRI+, Clinical pathology annotations for knee and brain fully sampled magnetic resonance imaging data R Zhao, B Yaman, Y Zhang, R Stewart, A Dixon, F Knoll, Z Huang, YW Lui, ... Scientific Data 9 (1), 152, 2022 | 68* | 2022 |
| Low-Rank Tensor Models for Improved Multidimensional MRI: Application to Dynamic Cardiac Mapping B Yaman, S Weingärtner, N Kargas, ND Sidiropoulos, M Akçakaya IEEE transactions on computational imaging 6, 194-207, 2019 | 58 | 2019 |
| Multi‐mask self‐supervised learning for physics‐guided neural networks in highly accelerated magnetic resonance imaging B Yaman, H Gu, SAH Hosseini, OB Demirel, S Moeller, J Ellermann, ... NMR in Biomedicine 35 (12), e4798, 2022 | 35 | 2022 |
| VLP: Vision Language Planning for Autonomous Driving C Pan, B Yaman, T Nesti, A Mallik, AG Allievi, S Velipasalar, L Ren Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 26 | 2024 |
| Self-supervised physics-guided deep learning reconstruction for high-resolution 3D LGE CMR B Yaman, C Shenoy, Z Deng, S Moeller, H El-Rewaidy, R Nezafat, ... 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 100-104, 2021 | 25 | 2021 |
| Revisiting -wavelet compressed-sensing MRI in the era of deep learning H Gu, B Yaman, S Moeller, J Ellermann, K Ugurbil, M Akçakaya Proceedings of the National Academy of Sciences 119 (33), e2201062119, 2022 | 18 | 2022 |
| Ground-truth free multi-mask self-supervised physics-guided deep learning in highly accelerated MRI B Yaman, SAH Hosseini, S Moeller, J Ellermann, K Uğurbil, M Akçakaya 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 1850-1854, 2021 | 18 | 2021 |
| Instabilities in conventional multi-coil MRI reconstruction with small adversarial perturbations C Zhang, J Jia, B Yaman, S Moeller, S Liu, M Hong, M Akçakaya 2021 55th Asilomar Conference on Signals, Systems, and Computers, 895-899, 2021 | 17 | 2021 |
| Ultra-sonic sensor based object detection for autonomous vehicles T Nesti, S Boddana, B Yaman Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2023 | 16 | 2023 |
| Zero-shot physics-guided deep learning for subject-specific MRI reconstruction B Yaman, SAH Hosseini, M Akcakaya NeurIPS 2021 Workshop on Deep Learning and Inverse Problems, 2021 | 16 | 2021 |
| 20-fold accelerated 7T fMRI using referenceless self-supervised deep learning reconstruction OB Demirel, B Yaman, L Dowdle, S Moeller, L Vizioli, E Yacoub, J Strupp, ... 2021 43rd Annual International Conference of the IEEE Engineering in …, 2021 | 16 | 2021 |
| Locally low-rank tensor regularization for high-resolution quantitative dynamic MRI B Yaman, S Weingärtner, N Kargas, ND Sidiropoulos, M Akçakaya 2017 IEEE 7th International Workshop on Computational Advances in Multi …, 2017 | 16 | 2017 |
| End-to-end AI-based MRI reconstruction and lesion detection pipeline for evaluation of deep learning image reconstruction R Zhao, Y Zhang, B Yaman, MP Lungren, MS Hansen arXiv preprint arXiv:2109.11524, 2021 | 14 | 2021 |
| Signal intensity informed multi‐coil encoding operator for physics‐guided deep learning reconstruction of highly accelerated myocardial perfusion CMR OB Demirel, B Yaman, C Shenoy, S Moeller, S Weingärtner, M Akçakaya Magnetic resonance in medicine 89 (1), 308-321, 2023 | 13 | 2023 |
Mehmet AkcakayaAssociate Professor, University of MinnesotaVerified email at umn.edu