Deep similarity learning for multimodal medical images X Cheng, L Zhang, Y Zheng Computer Methods in Biomechanics and Biomedical Engineering: Imaging …, 2016 | 232 | 2016 |
Quantitative elastography provided by surface acoustic waves measured by phase-sensitive optical coherence tomography C Li, G Guan, X Cheng, Z Huang, RK Wang Optics letters 37 (4), 722-724, 2012 | 122 | 2012 |
A unified approach to diffusion direction sensitive slice registration and 3-D DTI reconstruction from moving fetal brain anatomy M Fogtmann, S Seshamani, C Kroenke, X Cheng, T Chapman, J Wilm, ... IEEE transactions on medical imaging 33 (2), 272-289, 2013 | 102 | 2013 |
Deep similarity learning for multimodal medical images X Cheng, Z Li, Y Zheng US Patent 9,922,272, 2018 | 72 | 2018 |
A method for handling intensity inhomogenieties in fMRI sequences of moving anatomy of the early developing brain S Seshamani, X Cheng, M Fogtmann, ME Thomason, C Studholme Medical image analysis 18 (2), 285-300, 2014 | 40 | 2014 |
Cascaded slice to volume registration for moving fetal FMRI S Seshamani, M Fogtmann, X Cheng, M Thomason, C Gatenby, ... 2013 IEEE 10th International Symposium on Biomedical Imaging, 796-799, 2013 | 18 | 2013 |
Adapting parcellation schemes to study fetal brain connectivity in serial imaging studies X Cheng, J Wilm, S Seshamani, M Fogtmann, C Kroenke, C Studholme 2013 35th Annual International Conference of the IEEE Engineering in …, 2013 | 5 | 2013 |
Combining R2* Maps and Slice Registration for fMRI Analysis of Moving Subjects S Seshamani, C Gatenby, M Fogtmann, X Cheng, M Dighe, C Studholme The International Society for Magnetic Resonance in Medicine, 2013 | 2 | 2013 |
Combining R2* mapping and slice registration for fMRI analysis of moving subjects S Seshamani, C Gatenby, M Fogtmann, X Cheng, M Dighe, C Studholme Intl Soc Mag Reson Med 21, 3351, 2013 | 2 | 2013 |
Principal Component Analysis X Cheng, M Deng, AH Hormati US Patent App. 17/816,288, 2023 | 1 | 2023 |
Explainable artificial intelligence in computing environment X Cheng, L Yin, LIU Jiashang, AH Hormati, M Deng, CA Meyers US Patent App. 17/354,392, 2022 | 1 | 2022 |
Machine Learning Time Series Anomaly Detection LIU Jiashang, X Cheng, A Hormati, S Weijie US Patent App. 17/664,865, 2022 | 1 | 2022 |
Point anomaly detection Z Ye, LIU Jiashang, F Elliott, A Hormati, X Cheng, M Deng US Patent 11,928,017, 2024 | | 2024 |
Creating a machine learning model with k-means clustering M Deng, AH Hormati, X Cheng US Patent 11,842,291, 2023 | | 2023 |
Time Series Forecasting X Cheng, AH Hormati, L Yin, U Syed US Patent App. 18/323,766, 2023 | | 2023 |
Machine Learning Super Large-Scale Time-series Forecasting X Cheng, LIU Jiashang, L Yin, AH Hormati, M Deng, S Weijie, K Yousuf US Patent App. 17/652,863, 2023 | | 2023 |
Time series forecasting X Cheng, AH Hormati, L Yin, U Syed US Patent 11,693,867, 2023 | | 2023 |
Anomaly Detection with Local Outlier Factor X Cheng, Z Ye, P Lin, LIU Jiashang, A Hormati, M Deng US Patent App. 18/053,738, 2023 | | 2023 |
Machine Learning Regression Analysis X Cheng, L Yin, M Deng, A Hormati, UA Syed, LIU Jiashang US Patent App. 17/449,660, 2023 | | 2023 |
Creating a machine learning model with k-means clustering M Deng, AH Hormati, X Cheng US Patent 11,544,596, 2023 | | 2023 |