Kelvin Wong

Kelvin Wong, PhD

Associate Research Professor of Electronic Engineering in Oncology, Academic Institute
Associate Research Member, Research Institute
Houston Methodist


kwong@houstonmethodist.org
Biography

Dr. Wong is a biomedical engineer who specializes in developing new imaging systems and methods for the visualization of human disease. He received his Magnetic Resonance (MR) physics training at Columbia University in New York City, and in 2005, he joined the Functional and Molecular Imaging Center of the Brigham and Women’s Hospital and the HCNR Center of Bioinformatics in the Harvard Medical School as a Postdoctoral Fellow. In 2007, he joined the Houston Methodist Research Institute and is a faculty member of Weill Cornell Medicine since 2008.  

Description of Research

Dr. Wong’s current research interests focus on machine learning and deep learning in medical imaging. He is a Director of neuroimaging at TT and WF Chao Center for BRAIN for neurological disorders. His laboratory specialized in using deep learning derived features in addition to clinical features from electronic medical records for outcome prediction. Some examples include deep genomic features for glioblastoma patient survival modeling, deep imaging features for stroke patient outcome modeling.

Areas Of Expertise

Molecular imaging Cancer imaging Image-guided intervention
Education & Training

PhD, University of Hong Kong
M Phil, University of Hong Kong
Postdoctoral Fellowship, Harvard Medical School
Publications

Automatic Segmentation in Acute Ischemic Stroke: Prognostic Significance of Topological Stroke Volumes on Stroke Outcome
Wong, KK, Cummock, JS, Li, G, Ghosh, R, Xu, P, Volpi, JJ & Wong, STC 2022, , Stroke, vol. 53, no. 9, pp. 2896-2905. https://doi.org/10.1161/STROKEAHA.121.037982

DeepStroke: An efficient stroke screening framework for emergency rooms with multimodal adversarial deep learning
Cai, T, Ni, H, Yu, M, Huang, X, Wong, K, Volpi, J, Wang, JZ & Wong, STC 2022, , Medical Image Analysis, vol. 80, 102522. https://doi.org/10.1016/j.media.2022.102522

Image-to-image translation of label-free molecular vibrational images for a histopathological review using the UNet+/seg-cGAN model
He, Y, Li, J, Shen, S, Liu, K, WONG, KELVINK, He, T & WONG, STEPHENTC 2022, , Biomedical Optics Express, vol. 13, no. 4, pp. 1924-1938. https://doi.org/10.1364/BOE.445319

Patcher: Patch Transformers with Mixture of Experts for Precise Medical Image Segmentation
Ou, Y, Yuan, Y, Huang, X, Wong, ST, Volpi, J, Wang, JZ & Wong, K 2022, . in L Wang, Q Dou, PT Fletcher, S Speidel & S Li (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13435 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 475-484, 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Singapore, Singapore, 9/18/22. https://doi.org/10.1007/978-3-031-16443-9_46

Asymmetry Disentanglement Network for Interpretable Acute Ischemic Stroke Infarct Segmentation in Non-contrast CT Scans
Ni, H, Xue, Y, Wong, K, Volpi, J, Wong, STC, Wang, JZ & Huang, X 2022, . in L Wang, Q Dou, PT Fletcher, S Speidel & S Li (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13438 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 416-426, 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Singapore, Singapore, 9/18/22. https://doi.org/10.1007/978-3-031-16452-1_40

Retrospective study of deep learning to reduce noise in non-contrast head CT images
Wong, KK, Cummock, JS, He, Y, Ghosh, R, Volpi, JJ & Wong, STC 2021, , Computerized Medical Imaging and Graphics, vol. 94, 101996. https://doi.org/10.1016/j.compmedimag.2021.101996

An Intelligent Augmented Lifelike Avatar App for Virtual Physical Examination of Suspected Strokes
Yao, K, Wong, KK, Yu, X, Volpi, J & Wong, STC 2021, , Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, vol. 2021, pp. 1727-1730. https://doi.org/10.1109/EMBC46164.2021.9629720

Retrospective study of deep learning denoising to improve contrast-to-noise ratio in missed hyperacute ischemic stroke lesions
Ghosh, R, Cummock, JS, Wong, K, Volpi, JJ & Wong, ST 2021, , European Stroke Organization Conference 2021, 9/1/21 - 9/3/21 pp. 1717. https://doi.org/10.1177%2F23969873211034932

Severe Cerebral Small Vessel Disease Burden Is Associated With Poor Outcomes After Endovascular Thrombectomy in Acute Ischemic Stroke With Large Vessel Occlusion
Hooper, D, Nisar, T, McCane, D, Lee, J, Ling, KC, Vahidy, F, Wong, K, Wong, S, Chiu, D & Gadhia, R 2021, , Cureus, vol. 13, no. 2, pp. e13122. https://doi.org/10.7759/cureus.13122

Memory-Augmented Capsule Network for Adaptable Lung Nodule Classification
Mobiny, A, Yuan, P, Cicalese, PA, Moulik, SK, Garg, N, Wu, CC, Wong, K, Wong, ST, He, TC & Nguyen, HV 2021, , IEEE Transactions on Medical Imaging, vol. 40, no. 10, pp. 2869-2879. https://doi.org/10.1109/TMI.2021.3051089

LambdaUNet: 2.5D Stroke Lesion Segmentation of Diffusion-Weighted MR Images
Ou, Y, Yuan, Y, Huang, X, Wong, K, Volpi, J, Wang, JZ & Wong, STC 2021, . in M de Bruijne, M de Bruijne, PC Cattin, S Cotin, N Padoy, S Speidel, Y Zheng & C Essert (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12901 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 731-741, 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online, 9/27/21. https://doi.org/10.1007/978-3-030-87193-2_69

An Intelligent Augmented Lifelike Avatar App for Virtual Physical Examination of Suspected Strokes
Yao, K, Wong, KK, Yu, X, Volpi, J & Wong, STC 2021, . in 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc. pp. 1727-1730, 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021, Virtual, Online, Mexico, 11/1/21. https://doi.org/10.1109/EMBC46164.2021.9629720

A Time-Series Feature-Based Recursive Classification Model to Optimize Treatment Strategies for Improving Outcomes and Resource Allocations of COVID-19 Patients
Wang, L, Yin, Z, Puppala, M, Ezeana, C, Wong, K, He, T, Gotur, D & Wong, S 2022, , IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 7, pp. 3323-3329. https://doi.org/10.1109/JBHI.2021.3139773

Toward Rapid Stroke Diagnosis with Multimodal Deep Learning
Yu, M, Cai, T, Huang, X, Wong, K, Volpi, J, Wang, JZ & Wong, STC 2020, . in AL Martel, P Abolmaesumi, D Stoyanov, D Mateus, MA Zuluaga, SK Zhou, D Racoceanu & L Joskowicz (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12263 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 616-626, 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, Lima, Peru, 10/4/20. https://doi.org/10.1007/978-3-030-59716-0_59

Prognostic gene discovery in glioblastoma patients using deep learning
Wong, KK, Rostomily, R & Wong, STC 2019, , Cancers, vol. 11, no. 1, 53. https://doi.org/10.3390/cancers11010053

NIHSS Discrepancy and Reliability in Stroke Triage
Cummock, J, Wong, K, Wong, ST & Volpi, J 2019, , Neurovascular and Neurodegenerative Diseases, Paris, France, 10/28/19 - 10/30/19.

Magnetic resonance elastography measured shear stiffness as a biomarker of fibrosis in pediatric nonalcoholic fatty liver disease
Schwimmer, JB, Behling, C, Angeles, JE, Paiz, M, Durelle, J, Africa, J, Newton, KP, Brunt, EM, Lavine, JE, Abrams, SH, Masand, P, Krishnamurthy, R, Wong, K, Ehman, RL, Yin, M, Glaser, KJ, Dzyubak, B, Wolfson, T, Gamst, AC, Hooker, J, Haufe, W, Schlein, A, Hamilton, G, Middleton, MS & Sirlin, CB 2017, , Hepatology, vol. 66, no. 5, pp. 1474-1485. https://doi.org/10.1002/hep.29241, https://doi.org/10.1002/hep.29241

Subarachnoid hemorrhage – Induced block of cerebrospinal fluid flow: Role of brain coagulation factor III (tissue factor)
Golanov, EV, Bovshik, EI, Wong, KK, Pautler, RG, Foster, CH, Federley, RG, Zhang, JY, Mancuso, J, Wong, STC & Britz, GW 2018, , Journal of Cerebral Blood Flow and Metabolism, vol. 38, no. 5, pp. 793-808. https://doi.org/10.1177/0271678X17701157, https://doi.org/10.1177/0271678X17701157

Technical pitfalls of signal truncation in perfusion MRI of glioblastoma
Wong, KK, Fung, SH, New, PZ & Wong, STC 2016, , Frontiers in Neurology, vol. 7, no. AUG, 121, pp. 121. https://doi.org/10.3389/fneur.2016.00121

Dual CARS and SHG image acquisition scheme that combines single central fiber and multimode fiber bundle to collect and differentiate backward and forward generated photons
Weng, S, Chen, X, Xu, X, Wong, KK & Wong, STC 2016, , Biomedical Optics Express, vol. 7, no. 6, pp. 2202-2218. https://doi.org/10.1364/BOE.7.002202