Group Page: https://www.yanglab.fyi/
Call for Papers: Special Issue on Large Language Models (LLMs) and Cross-Modal Generative AI (https://www.computer.org/digital-library/journals/bd/cfp-llms-and-cross-modal-generative-ai) Please contact Lead Guest Editor: Dr Guang Yang for potential submissions.
Special issue call: “Federated Learning in Digital Healthcare: New Discoveries, Advances and Applications” (https://www.cell.com/patterns/special-issues/call-for-papers/federated-learning-in-digital-healthcare) in the Patterns journal (Cell Press). Please contact Lead Guest Editor: Dr Guang Yang for potential submissions.
Sensors Journal (Impact Factor: 3.576)
Deadline for manuscript submissions: 31 July 2023
Computational Imaging, Sensing and Analysis for Biomedical Applications
Frontiers in Cardiovascular Medicine (Impact Factor: 5.846)
Deadline for manuscript submissions: 02 February 2023
Generative Adversarial Networks in Cardiovascular Research
BMVC 2022 Proceedings (including e-posters/videos) are now available at: https://bmvc2022.org/programme/papers/
Dr. Guang Yang (B.Eng, M.Sc., Ph.D., IEEE Senior Member) 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 a UKRI Future Leaders Fellow (Round 5 Awardee) and Advanced Research Fellow working at National Heart and Lung Institute, Imperial College London and also affiliates with Cardiovascular Research Centre, Royal Brompton Hospital. He is also an Honorary Senior Lecturer with the School of Biomedical Engineering & Imaging Sciences, King's College London. He is a primary supervisor at the UKRI Centre for Doctoral Training in AI for Healthcare: AI4Health, KCL-Imperial EPSRC CDT in Smart Medical Imaging, and Imperial MRes Artificial Intelligence and Machine Learning programme.
He has worked for Siemens Medical Solutions and Medicsight PLC before and obtained comprehensive industrial experiences. He has two international patents 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, NIH and 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 the 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).
Guang has research outputs including publications in world-leading journals, e.g., 1 Nature Biomedical Engineering (IF: 28.1), 1 Nature Machine Intelligence (IF: 23.8), 1 Proceedings of the IEEE (IF: 20.6), 28 IEEE Trans, 3 Information Fusion (IF: 18.6), and 1 Radiology (IF: 19.7). By Aug 2023, Guang has published 116 journal articles, 61 peer-reviewed conference articles, 2 international patents, 3 books, and 3 book chapters with an H-index of 44 and an overall impact factor of 873.8. He has seven publications as Highly Cited Papers by the Web of Science, and 1 publication as the Most Popular Articles award 2020 in IEEE Transactions on Medical Imaging journal.
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 the 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 4 PDRAs, 7 PhD students, 6 BSc/MEng (UROP) students, 4 MRes students and co-supervising 7 additional PhD students with other PIs at the CDTs.
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.
Dr Guang Yang was the General Chair of MIUA 2022 (26th Medical Image Understanding and Analysis Conference). https://www.miua2022.com/
Dr Guang Yang was also the General Chair of BMVC 2022 (33rd British Machine Vision Conference). https://bmvc2022.org/
For journal special issue and conference call for papers please visit my Linkedin and Twitter (@gyangMedIA) for updated information. My group website is: https://www.yanglab.fyi/.
et al., 2023, Global transformer and dual local attention network via deep-shallow hierarchical feature fusion for retinal vessel segmentation, Ieee Transactions on Cybernetics, Vol:53, ISSN:2168-2275, Pages:5826-5839
et al., 2022, Unsupervised tissue segmentation via deep constrained Gaussian network, IEEE Transactions on Medical Imaging, Vol:41, ISSN:0278-0062, Pages:3799-3811
et al., 2022, Automatic fine-grained glomerular lesion recognition in kidney pathology, Pattern Recognition, Vol:127, ISSN:0031-3203
et al., 2022, Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions, Information Fusion, Vol:82, ISSN:1566-2535, Pages:99-122
et al., 2022, AI-based reconstruction for fast MRI – a systematic review and meta-analysis, Proceedings of the IEEE, Vol:110, ISSN:0018-9219, Pages:224-245
et al., 2022, Adaptive hierarchical dual consistency for semi-supervised left atrium segmentation on cross-domain data, IEEE Transactions on Medical Imaging, Vol:41, ISSN:0278-0062, Pages:420-433
Yang G, Ye Q, Xia J, 2022, Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond, Information Fusion, Vol:77, ISSN:1566-2535, Pages:29-52
et al., 2021, Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans, Nature Machine Intelligence, Vol:3, Pages:199-217
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 <i>p</i>:<i>q</i> 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