Kyungsang Kim
Kyungsang Kim
Assistant Professor at Harvard Medical School and Mass General Hospital
Verified email at - Homepage
Cited by
Cited by
Personalized iPSC-Derived Dopamine Progenitor Cells for Parkinson’s Disease
JS Schweitzer, B Song, TM Herrington, TY Park, N Lee, S Ko, J Jeon, ...
New England Journal of Medicine, 1926-1932, 2020
Iterative PET image reconstruction using convolutional neural network representation
K Gong, J Guan, K Kim, X Zhang, J Yang, Y Seo, G El Fakhri, J Qi, Q Li
IEEE transactions on medical imaging 38 (3), 675-685, 2018
Iterative low-dose CT reconstruction with priors trained by artificial neural network
D Wu, K Kim, G El Fakhri, Q Li
IEEE transactions on medical imaging 36 (12), 2479-2486, 2017
PET image denoising using unsupervised deep learning
J Cui, K Gong, N Guo, C Wu, X Meng, K Kim, K Zheng, Z Wu, L Fu, B Xu, ...
European journal of nuclear medicine and molecular imaging 46, 2780-2789, 2019
Penalized PET reconstruction using deep learning prior and local linear fitting
K Kim, D Wu, K Gong, J Dutta, JH Kim, YD Son, HK Kim, G El Fakhri, Q Li
IEEE transactions on medical imaging 37 (6), 1478-1487, 2018
Real-time visualization of 3-D dynamic microscopic objects using optical diffraction tomography
K Kim, KS Kim, HJ Park, JC Ye, YK Park
Optics express 21 (26), 32269-32278, 2013
Sparse-view spectral CT reconstruction using spectral patch-based low-rank penalty
K Kim, JC Ye, W Worstell, J Ouyang, Y Rakvongthai, G El Fakhri, Q Li
IEEE transactions on medical imaging 34 (3), 748-760, 2014
Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images
K Gong, J Yang, K Kim, G El Fakhri, Y Seo, Q Li
Physics in Medicine & Biology 63 (12), 125011, 2018
Motion adaptive patch-based low-rank approach for compressed sensing cardiac cine MRI
H Yoon, KS Kim, D Kim, Y Bresler, JC Ye
IEEE transactions on medical imaging 33 (11), 2069-2085, 2014
Computationally efficient deep neural network for computed tomography image reconstruction
D Wu, K Kim, Q Li
Medical physics 46 (11), 4763-4776, 2019
A cascaded convolutional neural network for x-ray low-dose CT image denoising
D Wu, K Kim, GE Fakhri, Q Li
arXiv preprint arXiv:1705.04267, 2017
Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19
A Zhong, X Li, D Wu, H Ren, K Kim, Y Kim, V Buch, N Neumark, B Bizzo, ...
Medical Image Analysis 70, 101993, 2021
Consensus neural network for medical imaging denoising with only noisy training samples
D Wu, K Gong, K Kim, X Li, Q Li
International Conference on Medical Image Computing and Computer-Assisted …, 2019
Fully 3D iterative scatter-corrected OSEM for HRRT PET using a GPU
KS Kim, JC Ye
Physics in Medicine & Biology 56 (15), 4991, 2011
Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study
S Ebrahimian, F Homayounieh, MABC Rockenbach, P Putha, T Raj, ...
Scientific Reports 11 (1), 858, 2021
Low‐dose CT reconstruction using spatially encoded nonlocal penalty
K Kim, G El Fakhri, Q Li
Medical physics 44 (10), e376-e390, 2017
MAPEM-Net: an unrolled neural network for Fully 3D PET image reconstruction
K Gong, D Wu, K Kim, J Yang, T Sun, G El Fakhri, Y Seo, Q Li
15th International meeting on fully three-dimensional image reconstruction …, 2019
Low‐dose CT reconstruction with Noise2Noise network and testing‐time fine‐tuning
D Wu, K Kim, Q Li
Medical Physics 48 (12), 7657-7672, 2021
Severity and consolidation quantification of COVID-19 from CT images using deep learning based on hybrid weak labels
D Wu, K Gong, CD Arru, F Homayounieh, B Bizzo, V Buch, H Ren, K Kim, ...
IEEE Journal of Biomedical and Health Informatics 24 (12), 3529-3538, 2020
Metal artifact reduction in CT by identifying missing data hidden in metals
HS Park, JK Choi, KR Park, KS Kim, SH Lee, JC Ye, JK Seo
Journal of X-ray science and technology 21 (3), 357-372, 2013
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