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David K. B. Li
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2020 – today
- 2020
- [j9]Hanwen Liu
, Qing-San Xiang, Roger C. Tam
Myelin water imaging data analysis in less than one minute. NeuroImage 210: 116551 (2020)
2010 – 2019
- 2019
- [j8]Youngjin Yoo, Lisa Y. W. Tang, David K. B. Li, Luanne Metz, Shannon H. Kolind, Anthony Traboulsee, Roger C. Tam
Deep learning of brain lesion patterns and user-defined clinical and MRI features for predicting conversion to multiple sclerosis from clinically isolated syndrome. Comput. methods Biomech. Biomed. Eng. Imaging Vis. 7(3): 250-259 (2019) - 2017
- [c7]Youngjin Yoo, Lisa Y. W. Tang, Su-Hyun Kim, Ho Jin Kim, Lisa Eunyoung Lee
Hierarchical Multimodal Fusion of Deep-Learned Lesion and Tissue Integrity Features in Brain MRIs for Distinguishing Neuromyelitis Optica from Multiple Sclerosis. MICCAI (3) 2017: 480-488 - 2016
- [j7]Tom Brosch, Lisa Y. W. Tang
Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation. IEEE Trans. Medical Imaging 35(5): 1229-1239 (2016) - [c6]Youngjin Yoo, Lisa Y. W. Tang, Tom Brosch, David K. B. Li, Luanne Metz, Anthony Traboulsee, Roger C. Tam
Deep Learning of Brain Lesion Patterns for Predicting Future Disease Activity in Patients with Early Symptoms of Multiple Sclerosis. LABELS/DLMIA@MICCAI 2016: 86-94 - [c5]Lisa Y. W. Tang
Corpus Callosum Segmentation in Brain MRIs via Robust Target-Localization and Joint Supervised Feature Extraction and Prediction. MICCAI (2) 2016: 406-414 - 2015
- [c4]Tom Brosch, Youngjin Yoo, Lisa Y. W. Tang, David K. B. Li, Anthony Traboulsee, Roger C. Tam
Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation. MICCAI (3) 2015: 3-11 - [c3]Lisa Y. W. Tang, Ghassan Hamarneh
Corpus Callosum Segmentation in MS Studies Using Normal Atlases and Optimal Hybridization of Extrinsic and Intrinsic Image Cues. MICCAI (3) 2015: 123-131 - 2014
- [c2]Youngjin Yoo, Tom Brosch, Anthony Traboulsee, David K. B. Li, Roger C. Tam
Deep Learning of Image Features from Unlabeled Data for Multiple Sclerosis Lesion Segmentation. MLMI 2014: 117-124 - [c1]Tom Brosch, Youngjin Yoo, David K. B. Li, Anthony Traboulsee
Modeling the Variability in Brain Morphology and Lesion Distribution in Multiple Sclerosis by Deep Learning. MICCAI (2) 2014: 462-469 - 2011
- [j6]Erin L. MacMillan, Burkhard Mädler, Nicole Fichtner, Marcel F. Dvorak
Myelin water and T2 relaxation measurements in the healthy cervical spinal cord at 3.0T: Repeatability and changes with age. NeuroImage 54(2): 1083-1090 (2011) - 2010
- [j5]Jon McAusland, Roger C. Tam
Optimizing the Use of Radiologist Seed Points for Improved Multiple Sclerosis Lesion Segmentation. IEEE Trans. Biomed. Eng. 57(11): 2689-2698 (2010)
2000 – 2009
- 2009
- [j4]Roger C. Tam
Detection and measurement of coverage loss in interleaved multi-acquisition brain MRIs due to motion-induced inter-slice misalignment. Medical Image Anal. 13(3): 381-391 (2009) - 2008
- [j3]Shannon H. Kolind, Cornelia Laule, Irene M. Vavasour, David K. B. Li, Anthony Traboulsee
Complementary information from multi-exponential T2 relaxation and diffusion tensor imaging reveals differences between multiple sclerosis lesions. NeuroImage 40(1): 77-85 (2008) - [j2]Cornelia Laule, Piotr Kozlowski, Esther Leung, David K. B. Li, Alex L. MacKay, G. R. Wayne Moore:
Myelin water imaging of multiple sclerosis at 7 T: Correlations with histopathology. NeuroImage 40(4): 1575-1580 (2008) - 2006
- [j1]Irene M. Vavasour, Campbell M. Clark, David K. B. Li, Alex L. MacKay:
Reproducibility and reliability of MR measurements in white matter: Clinical implications. NeuroImage 32(2): 637-642 (2006)
Coauthor Index
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