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., 2019, Deep learning for diagnosis of chronic myocardial infarction on nonenhanced cardiac cine MRI, Radiology, ISSN:0033-8419
et al., 2019, Tissue-type mapping of gliomas, Neuroimage-clinical, Vol:21, ISSN:2213-1582
et al., 2018, Stochastic deep compressive sensing for the reconstruction of diffusion tensor cardiac MRI, Lecture Notes in Bioinformatics, Vol:11070 LNCS, ISSN:0302-9743, Pages:295-303
et al., 2018, DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction, IEEE Transactions on Medical Imaging, Vol:37, ISSN:0278-0062, Pages:1310-1321
et al., 2018, Fully automatic segmentation and objective assessment of atrial scars for long-standing persistent atrial fibrillation patients using late gadolinium-enhanced MRI., Med Phys, Vol:45, Pages:1562-1576
et al., 2018, Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels, Computer Methods and Programs in Biomedicine, Vol:157, ISSN:0169-2607, Pages:69-84
et al., 2016, 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., 2015, 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, 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., 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., 2018, Multiview sequential learning and dilated residual learning for a fully automatic delineation of the left atrium and pulmonary veins from late gadolinium-enhanced cardiac MRI images, 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, Pages:1123-1127, ISSN:1557-170X
et al., 2018, Atrial Fibrosis Quantification Based on Maximum Likelihood Estimator of Multivariate Images, 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2018), Pages:604-612, ISSN:0302-9743
et al., 2018, The Deep Poincaré Map: A Novel Approach for Left Ventricle Segmentation, 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2018), Pages:561-568, ISSN:0302-9743
et al., 2018, Bayesian VoxDRN: A Probabilistic Deep Voxelwise Dilated Residual Network for Whole Heart Segmentation from 3D MR Images, 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2018), Pages:569-577, ISSN:0302-9743
et al., 2018, Adversarial and perceptual refinement for compressed sensing MRI reconstruction, 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2018), Pages:232-240, ISSN:0302-9743
et al., 2017, Segmenting atrial fibrosis from late gadolinium-enhanced cardiac MRI by deep-learned features with stacked sparse auto-encoders, MIUA 2017, Springer, Pages:195-206, ISSN:1865-0929
et al., 2017, A fully automatic deep learning method for atrial scarring segmentation from late gadolinium-enhanced MRI images, 2017 IEEE 14th International Symposium on Biomedical Imaging, IEEE, Pages:844-848, ISSN:1945-7928
et al., 2016, Combined self-learning based single-image super-resolution and dual-tree complex wavelet transform denoising for medical images, Medical Imaging 2016: Image Processing, Society of Photo Optical Instrumentation Engineers
et al., 2016, Super-Resolved Enhancement of a Single Image and Its Application in Cardiac MRI, Pages:179-190
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