| Reconfigurable heterogeneous integration using stackable chips with embedded artificial intelligence C Choi, H Kim, JH Kang, MK Song, H Yeon, CS Chang, JM Suh, J Shin, ... Nature Electronics 5 (6), 386-393, 2022 | 93 | 2022 |
| Deep learning in MR image processing D Lee, J Lee, J Ko, J Yoon, K Ryu, Y Nam investigative magnetic resonance imaging 23 (2), 81-99, 2019 | 58 | 2019 |
| Biomedical image analysis competitions: The state of current participation practice M Eisenmann, A Reinke, V Weru, MD Tizabi, F Isensee, TJ Adler, ... arXiv preprint arXiv:2212.08568, 2022 | 37 | 2022 |
| Artificial neural network for multi‐echo gradient echo–based myelin water fraction estimation S Jung, H Lee, K Ryu, JE Song, M Park, WJ Moon, DH Kim Magnetic resonance in medicine 85 (1), 380-389, 2021 | 29 | 2021 |
| Why is the winner the best? M Eisenmann, A Reinke, V Weru, MD Tizabi, F Isensee, TJ Adler, S Ali, ... Proceedings of the IEEE/CVF conference on computer vision and Pattern …, 2023 | 28 | 2023 |
| Data‐driven synthetic MRI FLAIR artifact correction via deep neural network K Ryu, Y Nam, SM Gho, J Jang, HJ Lee, J Cha, HJ Baek, J Park, DH Kim Journal of Magnetic Resonance Imaging 50 (5), 1413-1423, 2019 | 26 | 2019 |
| Synthesizing T1 weighted MPRAGE image from multi echo GRE images via deep neural network K Ryu, NY Shin, DH Kim, Y Nam Magnetic Resonance Imaging 64, 13-20, 2019 | 21 | 2019 |
| Regulation of root patterns in mammalian teeth H Seo, J Kim, JJ Hwang, HG Jeong, SS Han, W Park, K Ryu, H Seomun, ... Scientific reports 7 (1), 12714, 2017 | 19 | 2017 |
| Stenosis detection from time-of-flight magnetic resonance angiography via deep learning 3d squeeze and excitation residual networks H Chung, KM Kang, MA Al-Masni, CH Sohn, Y Nam, K Ryu, DH Kim IEEE Access 8, 43325-43335, 2020 | 18 | 2020 |
| Improving phase‐based conductivity reconstruction by means of deep learning–based denoising of phase data for 3T MRI KJ Jung, S Mandija, JH Kim, K Ryu, S Jung, C Cui, SY Kim, M Park, ... Magnetic Resonance in Medicine 86 (4), 2084-2094, 2021 | 17 | 2021 |
| Estimating age-related changes in in vivo cerebral magnetic resonance angiography using convolutional neural network Y Nam, J Jang, HY Lee, Y Choi, NY Shin, KH Ryu, DH Kim, SL Jung, ... Neurobiology of Aging 87, 125-131, 2020 | 12 | 2020 |
| Reduction of respiratory motion artifact in c-spine imaging using deep learning: Is substitution of navigator possible H Lee, K Ryu, Y Nam, J Lee, DH Kim Proceedings of the ISMRM Scientific Meeting & Exhibition, Paris 2660, 2018 | 11 | 2018 |
| Development of a deep learning method for phase unwrapping MR images K Ryu, SM Gho, Y Nam, K Koch, DH Kim Proc Int. Soc. Magn. Reson. Med 27, 4707, 2019 | 10 | 2019 |
| Accelerated multicontrast reconstruction for synthetic MRI using joint parallel imaging and variable splitting networks K Ryu, JH Lee, Y Nam, SM Gho, HS Kim, DH Kim Medical physics 48 (6), 2939-2950, 2021 | 8 | 2021 |
| K-space refinement in deep learning mr reconstruction via regularizing scan specific spirit-based self consistency K Ryu, C Alkan, C Choi, I Jang, S Vasanawala Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 7 | 2021 |
| Validation of deep learning-based artifact correction on synthetic FLAIR images in a different scanning environment KH Ryu, HJ Baek, SM Gho, K Ryu, DH Kim, SE Park, JY Ha, SB Cho, ... Journal of clinical medicine 9 (2), 364, 2020 | 6 | 2020 |
| Adaptive weighted polynomial fitting in phase-based electrical property tomography JH Kim, J Shin, HJ Lee, KH Ryu, D Kim Proc. Intl. Soc. Mag. Reson. Med. 25, 3643, 2017 | 6 | 2017 |
| Multi-planar 2.5 D U-Net for image quality enhancement of dental cone-beam CT K Ryu, C Lee, Y Han, S Pang, YH Kim, C Choi, I Jang, SS Han Plos one 18 (5), e0285608, 2023 | 5 | 2023 |
| Improving high frequency image features of deep learning reconstructions via k‐space refinement with null‐space kernel K Ryu, C Alkan, SS Vasanawala Magnetic resonance in medicine 88 (3), 1263-1272, 2022 | 5 | 2022 |
| Multi-task accelerated mr reconstruction schemes for jointly training multiple contrasts V Liu, K Ryu, C Alkan, JM Pauly, S Vasanawala NeurIPS 2021 Workshop on Deep Learning and Inverse Problems, 2021 | 4 | 2021 |
Dong-Hyun KimDept. Electrical and Electronic Eng, Yonsei UniversityEmail được xác minh tại yonsei.ac.kr
