Dr. Guang Yang (B.Eng, M.Sc., Ph.D., M.IEEE, M.ISMRM, M.SPIE) obtained his M.Sc. in Vision Imaging and Virtual Environments from the Department of Computer Science in 2006 and his Ph.D. on medical image analysis jointly from the CMIC, Department of Computer Science and Medical Physics in 2012 both from University College London.
He is currently an honorary lecturer with the Neuroscience Research Centre, Cardiovascular and Cell Sciences Institute, St. George’s, University of London. He is also an image processing physicist and honorary senior research fellow working at Cardiovascular Research Centre, Royal Brompton Hospital and also affiliate with National Heart and Lung Institute, Imperial College London.
He has worked for Siemens Medical Solutions and Medicsight PLC before, and obtained comprehensive industrial experiences. He leads two international patent applications in pending in the fields of medical image processing. His research collaborators are from Imperial College London, St. George’s, University of London, University of Lincoln, City University London, University College London, Fudan University, and Shanghai Jiao Tong University in China.
He has participated in many medical image analysis projects including: breast tumour image analysis using digital breast tomosynthesis (funded by Department of Trade and Industry and EPSRC); colon cancer computer aided diagnosis and detection using CT imaging (funded by TSB); multimodal advanced MRI analysis for brain tumour grading, classification, growth modelling and therapy planning (funded by CRUK).
At National Heart and Lung Institute, he was working on a cardiac MRI project funded by NIHR. Recently, he proposed a novel workflow to achieve fast acquisition, superior image quality and quantitative analysis for the late gadolinium enhancement (LGE) MRI images, which could help diagnosis, treatment planning and prognosis of atrial fibrillation patients. For this proposal, Dr. Guang Yang (Co-I) has been awarded a British Heart Foundation project grant together with Dr. Jennifer Keegan (PI), Prof. David Firmin (Co-I), Prof. Raad Mohiaddin (Co-I), and Dr. Tom Wong (Co-I).
et al., Supervised Learning based Multimodal MRI Brain Tumour Segmentation using Texture Features from Supervoxels, Computer Methods and Programs in Biomedicine, ISSN:0169-2607
et al., 2017, DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction, IEEE Transactions on Medical Imaging, ISSN:0278-0062
et al., 2017, Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI, International Journal of Computer Assisted Radiology and Surgery, Vol:12, ISSN:1861-6410, Pages:183-203
et al., 2016, On the averaging of cardiac diffusion tensor MRI data: the effect of distance function selection., Phys Med Biol, Vol:61, Pages:7765-7786
et al., 2016, Morphometric Model for Discrimination between Glioblastoma Multiforme and Solitary Metastasis Using Three-Dimensional Shape Analysis, Magnetic Resonance in Medicine, Vol:75, ISSN:0740-3194, Pages:2505-2516
et al., 2015, Discrete Wavelet Transform Based Whole-Spectral and Sub-Spectral Analysis for Improved Brain Tumour Clustering using Single Voxel MR Spectroscopy, IEEE Transactions on Biomedical Engineering, Vol:62, ISSN:0018-9294, Pages:2860-2866
et al., 2014, Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering, Magnetic Resonance in Medicine, Vol:74, ISSN:0740-3194, Pages:868-878
et al., 2014, Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique, Neuro-Oncology, Vol:17, ISSN:1522-8517, Pages:466-476
et al., 2014, Discrimination between glioblastoma multiforme and solitary metastasis using morphological features derived from the p:q tensor decomposition of diffusion tensor imaging, NMR in Biomedicine, Vol:27, ISSN:0952-3480, Pages:1103-1111
et al., 2016, Super-Resolved Enhancement of a Single Image and Its Application in Cardiac MRI, 7th International Conference on Image and Signal Processing (ICISP), SPRINGER INT PUBLISHING AG, Pages:179-190, ISSN:0302-9743
et al., 2012, Joint registration and limited-angle reconstruction of digital breast tomosynthesis, Pages:713-720, ISSN:0302-9743
et al., 2014, Classification of brain tumour 1H MR spectra: Extracting features by metabolite quantification or nonlinear manifold learning?, IEEE The International Symposium on Biomedical Imaging (ISBI), The Institute of Electrical and Electronics Engineers (IEEE), Pages:1039-1042
et al., 2010, Combined Reconstruction and Registration of Digital Breast Tomosynthesis, 10th International Workshop on Digital Mammography, SPRINGER-VERLAG BERLIN, Pages:760-+, ISSN:0302-9743