Dr. Guang Yang (B.Eng, M.Sc., Ph.D., M.IEEE, M.BMVA, 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 senior research fellow and MRI processing physicist working at Cardiovascular Research Centre, Royal Brompton Hospital, and also affiliate with National Heart and Lung Institute, Imperial College London. He is also an honorary lecturer with the Neuroscience Research Centre, Cardiovascular and Cell Sciences Institute, St. George’s, University of 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 Cambridge University, Oxford University, St. George’s, University of London, University of Lincoln, City University London, and University College London in the UK, UCLA in the USA, Fudan University, Shanghai Jiao Tong University, Capital Medical University and Sun Yat-sen 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).
During his research career, he has published over 120 publications including 50 Journal Articles (13 IEEE Trans, 2 MedIA, 2 Information Fusion, 1 Radiology, etc. 2 of them are Highly Cited Papers by the Web of Science, 1 of them is the Most Popular Articles 2020 of the IEEE TMI), 40 Peer-Reviewed Conference Articles, 2 International Patents, 2 Books, 1 Book Chapter with an H-index of 22 and Overall Impact Factors of 258.89 (by Feb 2021).
At the National Heart and Lung Institute, he was working on a cardiac MRI project funded by NIHR. Recently, he has successfully finished his British Heart Foundation funded project (Co-PI, PG/16/78/32402, 2017-2019) on fast acquisition and quantitative analysis for the late gadolinium enhancement MRI images. He is now working on EU European Research Council funded H2020 CHAIMELEON project (PI of the workstream, H2020-SC1-FA-DTS-2019-1 952172, 2020-2023) and IMI DRAGON project (PI of the workstream, H2020-JTI-IMI2 101005122, 2020-2023). He is now supervising 3 PhD, 2 BSc/Msc students and co-supervising 3 additional PhD students.
He is an investigator of the AI Assisted Diagnosis and Prognostications in Covid-19 team led by Cambridge CMIH.
He is on the advisory board of Aladdin Healthcare Technologies, and has industrial collaborations with NVidia and Boehringer Ingelheim.
Please contribute to our special track on XAI models in Medical Imaging in conjunction with the 34th IEEE CBMS International Symposium on Computer-Based Medical Systems. Submission deadline: Feb 5, 2021, Acceptance: March 26, 2021. https://essexnlip.uk/cbms2021/
et al., 2021, Multitask Learning for Estimating Multitype Cardiac Indices in MRI and CT Based on Adversarial Reverse Mapping, IEEE Transaction on Neural Networks and Learning Systems, Vol:32, ISSN:2162-237X, Pages:493-506
et al., 2020, Catheter ablation vs. thoracoscopic surgical ablation in long-standing persistent atrial fibrillation: CASA-AF randomized controlled trial., European Heart Journal, Vol:41, ISSN:0195-668X, Pages:4471-4480
et al., 2020, Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention, Future Generation Computer Systems-the International Journal of Grid Computing and Escience, Vol:107, ISSN:0167-739X, Pages:215-228
et al., 2020, MV-RAN: Multiview recurrent aggregation network for echocardiographic sequences segmentation and full cardiac cycle analysis, Computers in Biology and Medicine, Vol:120, ISSN:0010-4825
et al., 2019, Deep learning for diagnosis of chronic myocardial infarction on nonenhanced cardiac cine MRI, Radiology, Vol:294, ISSN:0033-8419, Pages:52-60
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