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Ge Wang
Ge Wang
Rensselaer Polytechnic Institute
Подтвержден адрес электронной почты в домене rpi.edu - Главная страница
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Процитировано
Процитировано
Год
Low-dose CT with a residual encoder-decoder convolutional neural network
H Chen, Y Zhang, MK Kalra, F Lin, Y Chen, P Liao, J Zhou, G Wang
IEEE transactions on medical imaging 36 (12), 2524-2535, 2017
17822017
Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss
Q Yang, P Yan, Y Zhang, H Yu, Y Shi, X Mou, MK Kalra, Y Zhang, L Sun, ...
IEEE transactions on medical imaging 37 (6), 1348-1357, 2018
16772018
Low-dose CT via convolutional neural network
H Chen, Y Zhang, W Zhang, P Liao, K Li, J Zhou, G Wang
Biomedical optics express 8 (2), 679-694, 2017
8382017
Low-dose X-ray CT reconstruction via dictionary learning
Q Xu, H Yu, X Mou, L Zhang, J Hsieh, G Wang
IEEE Transactions on Medical Imaging 31 (9), 1682-1697, 2012
7682012
Compressed sensing based interior tomography
H Yu, G Wang
Physics in medicine & biology 54 (9), 2791, 2009
7092009
A perspective on deep imaging
G Wang
IEEE Access 4, 8914-8924, 2016
5552016
A general cone-beam reconstruction algorithm
G Wang, TH Lin, P Cheng, DM Shinozaki
IEEE Transactions on Medical Imaging 12 (3), 486-496, 1993
5551993
On interpretability of artificial neural networks: A survey
FL Fan, J Xiong, M Li, G Wang
IEEE Transactions on Radiation and Plasma Medical Sciences 5 (6), 741-760, 2021
534*2021
CT super-resolution GAN constrained by the identical, residual, and cycle learning ensemble (GAN-CIRCLE)
C You, G Li, Y Zhang, X Zhang, H Shan, M Li, S Ju, Z Zhao, Z Zhang, ...
IEEE transactions on medical imaging 39 (1), 188-203, 2019
5312019
Iterative deblurring for CT metal artifact reduction
G Wang, DL Snyder, JA O'Sullivan, MW Vannier
IEEE transactions on medical imaging 15 (5), 657-664, 1996
5281996
Image Reconstruction Is a New Frontier of Machine Learning
G Wang, JC Ye, K Mueller, JA Fessler
IEEE transactions on medical imaging 37 (6), 1289-1296, 2018
5222018
Deep learning for tomographic image reconstruction
G Wang, JC Ye, B De Man
Nature Machine Intelligence 2 (12), 737-748, 2020
5092020
3-D convolutional encoder-decoder network for low-dose CT via transfer learning from a 2-D trained network
H Shan, Y Zhang, Q Yang, U Kruger, MK Kalra, L Sun, W Cong, G Wang
IEEE transactions on medical imaging 37 (6), 1522-1534, 2018
5052018
An outlook on x‐ray CT research and development
G Wang, H Yu, B De Man
Medical physics 35 (3), 1051-1064, 2008
4552008
LEARN: Learned experts’ assessment-based reconstruction network for sparse-data CT
H Chen, Y Zhang, Y Chen, J Zhang, W Zhang, H Sun, Y Lv, P Liao, J Zhou, ...
IEEE transactions on medical imaging 37 (6), 1333-1347, 2018
4432018
Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction
H Shan, A Padole, F Homayounieh, U Kruger, RD Khera, C Nitiwarangkul, ...
Nature Machine Intelligence 1 (6), 269-276, 2019
4072019
Standard handbook of biomedical engineering & design
M Kutz
McGraw-Hill Education, 2003
4072003
A roadmap for foundational research on artificial intelligence in medical imaging: from the 2018 NIH/RSNA/ACR/The Academy Workshop
CP Langlotz, B Allen, BJ Erickson, J Kalpathy-Cramer, K Bigelow, ...
Radiology 291 (3), 781-791, 2019
3892019
Uniqueness theorems in bioluminescence tomography
G Wang, Y Li, M Jiang
Medical physics 31 (8), 2289-2299, 2004
3772004
Practical reconstruction method for bioluminescence tomography
W Cong, G Wang, D Kumar, Y Liu, M Jiang, LV Wang, EA Hoffman, ...
Optics Express 13 (18), 6756-6771, 2005
3622005
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