Synho Do, Ph.D.
Affiliations: | University of Southern California, Los Angeles, CA, United States |
Area:
Neuronal Systems ModelingGoogle:
"Synho Do"Mean distance: 18.1 (cluster 17)
Parents
Sign in to add mentor| Vasilis Z. Marmarelis | grad student | 2004 | USC | |
| (Volterra -type nonlinear filtering for medical imaging enhancements using principal dynamic modes.) | ||||
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Publications
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| Sim Y, Chung MJ, Kotter E, et al. (2019) Deep Convolutional Neural Network-based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs. Radiology. 182465 |
| Lee H, Huang C, Yune S, et al. (2019) Machine Friendly Machine Learning: Interpretation of Computed Tomography Without Image Reconstruction. Scientific Reports. 9: 15540 |
| Lee H, Yune S, Mansouri M, et al. (2019) An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets. Nature Biomedical Engineering. 3: 173-182 |
| Choy G, Khalilzadeh O, Michalski M, et al. (2018) Current Applications and Future Impact of Machine Learning in Radiology. Radiology. 171820 |
| Thrall JH, Li X, Li Q, et al. (2018) Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success. Journal of the American College of Radiology : Jacr |
| Lee H, Mansouri M, Tajmir S, et al. (2017) A Deep-Learning System for Fully-Automated Peripherally Inserted Central Catheter (PICC) Tip Detection. Journal of Digital Imaging |
| Lee H, Troschel FM, Tajmir S, et al. (2017) Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis. Journal of Digital Imaging |
| Lee H, Tajmir S, Lee J, et al. (2017) Fully Automated Deep Learning System for Bone Age Assessment. Journal of Digital Imaging |
| Puchner SB, Ferencik M, Maehara A, et al. (2017) Iterative Image Reconstruction Improves the Accuracy of Automated Plaque Burden Assessment in Coronary CT Angiography: A Comparison With Intravascular Ultrasound. Ajr. American Journal of Roentgenology. 1-8 |
| Khawaja RDA, Singh S, Blake M, et al. (2016) Corrigendum to "Ultra-low dose abdominal MDCT: Using a knowledge-based Iterative Model Reconstruction technique for substantial dose reduction in a prospective clinical study" [Eur. J. Radiol. 84 (2015) 2-10]. European Journal of Radiology. 85: 310 |