Stephen T. C. Wong focuses on Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Cancer research. His research in Artificial intelligence intersects with topics in Tracing and Bioinformatics. His Computer vision study integrates concerns from other disciplines, such as Cerebral cortex, Representation, Surface and Brain mri.
His studies in Pattern recognition integrate themes in fields like Fractional anisotropy, Parametric statistics and Brain tissue. The study incorporates disciplines such as Effective diffusion coefficient, Diffusion MRI, Cerebrospinal fluid and White matter in addition to Segmentation. His Cancer research study combines topics from a wide range of disciplines, such as Metastasis and Immunology.
His main research concerns Artificial intelligence, Computer vision, Cancer, Cancer research and Pattern recognition. His Machine learning research extends to the thematically linked field of Artificial intelligence. His research links Dendritic spine with Computer vision.
His Cancer research includes themes of Bioinformatics and Pathology. Stephen T. C. Wong interconnects Cancer cell, Tumor progression, Signal transduction and Metastasis in the investigation of issues within Cancer research. Stephen T. C. Wong works on Segmentation which deals in particular with Scale-space segmentation.
Cancer research, Internal medicine, Cancer, Artificial intelligence and Cancer cell are his primary areas of study. Stephen T. C. Wong has included themes like Tumor progression, Ovarian tumor, Ovarian cancer and Metastasis in his Cancer research study. In his research on the topic of Internal medicine, Medulloblastoma is strongly related with Oncology.
Stephen T. C. Wong combines subjects such as Cell and Drug with his study of Cancer. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning, Lung cancer and Pattern recognition. His study on Cancer cell also encompasses disciplines like
Stephen T. C. Wong mainly focuses on Cancer research, Breast cancer, Cancer, Cancer cell and Tumor microenvironment. His Cancer research research is multidisciplinary, incorporating elements of Cell, Signal transduction, Stem cell, Gene silencing and Triple-negative breast cancer. The Breast cancer study combines topics in areas such as Phenotype, Cell culture and Artificial intelligence.
His Artificial intelligence research is multidisciplinary, relying on both Scale parameter and Energy functional. Stephen T. C. Wong studies Cancer, focusing on Metastasis in particular. His research integrates issues of Platelet, Ovarian cancer, Bone metastasis and Danusertib in his study of Cancer cell.
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MeCP2, a key contributor to neurological disease, activates and represses transcription.
Maria Chahrour;Sung Yun Jung;Chad Shaw;Xiaobo Zhou.
Epithelial-to-mesenchymal transition is not required for lung metastasis but contributes to chemoresistance
Kari R. Fischer;Anna Durrans;Sharrell Lee;Jianting Sheng.
Breast cancer stem cells transition between epithelial and mesenchymal states reflective of their normal counterparts
Suling Liu;Yang Cong;Dong Wang;Yu-Min Sun.
Stem cell reports (2014)
Web-based, biometric authentication system and method
Yuan-Pin Yu;Stephen Wong;Mark B. Hoffberg.
Big Data for Health
Javier Andreu-Perez;Carmen C. Y. Poon;Robert D. Merrifield;Stephen T. C. Wong.
IEEE Journal of Biomedical and Health Informatics (2015)
Antiproliferative Effects of 1,25-Dihydroxyvitamin D3 on Primary Cultures of Human Prostatic Cells
Donna M. Peehl;Roman J. Skowronski;Gordon K. Leung;Stephen T. Wong.
Cancer Research (1994)
Personalizing hospital intranet web sites
Mehran Moshfeghi;Jun Wang;Stephen T. C. Wong;Yuan-Pin Yu.
Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy
Xiaowei Chen;Xiaobo Zhou;S.T.C. Wong.
IEEE Transactions on Biomedical Engineering (2006)
Radiologic image compression-a review
S. Wong;L. Zaremba;D. Gooden;H.K. Huang.
Proceedings of the IEEE (1995)
Diffusion-weighted and fluid-attenuated inversion recovery imaging in Creutzfeldt-Jakob disease: high sensitivity and specificity for diagnosis.
Geoffrey S. Young;Michael D. Geschwind;Nancy J. Fischbein;Jennifer L. Martindale.
American Journal of Neuroradiology (2005)
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