Professor Greg Slabaugh

Profile

Greg is Professor of Computer Vision and AI and director of the newly formed Digital Environment Research Institute (DERI) at Queen Mary. His primary research interests include computer vision, deep learning, computational photography, medical image computing.

Prior to joining Queen Mary University of London in 2020, he was Chief Scientist in Computer Vision (EU) for Huawei Technologies R&D where he led a team of research scientists working in computational photography, studying the camera image signal processor (ISP) pipeline including denoising, demosaicing, automatic white balance, super-resolution, and colour enhancement for high quality photographs and video.  Earlier industrial appointments include Medicsight, where he led a team of research scientists in detection of pre-cancerous lesions in the colon and lung, imaged with computed tomography; with the companys ColonCAD product receiving FDA clearance and CE marking.  He also was an employee of Siemens, where he performed research in medical image computing and 3D shape modelling. He holds 36 granted patents and has over 150 publications.

He earned a PhD in Electrical Engineering from Georgia Institute of Technology in Atlanta, USA where his thesis focused on reconstruction of 3D shapes from 2D photographs.  For six years he was an academic at City, University of London where he taught modules in computer vision, graphics, computer games technology, and programming in addition to leading research grants funded by the European Commission, EPSRC and Innovate UK.  He was awarded a university-wide Research Student Supervision Award in 2017, and a Teaching in the Schools award for the School of Mathematics, Computer Science, and Engineering in 2016.

Prof Slabaugh is also the Alan Turing Institute's Turing (Academic) Liaison on behalf of Queen Mary, as part of the University's role in the Turing University Network.

Research

Publications

• Gaintseva T, Benning M, Slabaugh G (2025). RAVE: Residual Vector Embedding for CLIP-Guided Backlit Image Enhancement. nameOfConference

  
  

  DOI: 10.1007/978-3-031-72986-7_24

  
  

  QMRO: qmroHref
• Jaffery OA, Zolotarev A, Barera CE et al. (2024). An in-silico comparison of anatomical and substrate based AF ablation using electro and optical flow mapping. nameOfConference

  
  

  DOI: 10.1093/eurheartj/ehae666.579

  
  

  QMRO: qmroHref
• Cheng F, Rauseo E, Misghina S et al. (2024). Biventricular modelling for investigating ventricular arrhythmias in silico. nameOfConference

  
  

  DOI: 10.1093/eurheartj/ehae666.3501

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/101363
• Misghina S, Rauseo E, Barrera CE et al. (2024). In-silico insights into personalised therapy selection of anti-arrhythmic drugs and ablation using patient-specific biatrial models for atrial fibrillation. nameOfConference

  
  

  DOI: 10.1093/eurheartj/ehae666.3504

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/101360
• Bransby KM, Bajaj R, Ramasamy A et al. (2024). POLYCORE: Polygon-based contour refinement for improved Intravascular Ultrasound Segmentation. nameOfConference

  
  

  DOI: 10.1016/j.compbiomed.2024.109162

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/99711
• Chadalavada S, Rauseo E, Salih A et al. (2024). Quality control of cardiac magnetic resonance imaging segmentation, feature tracking, aortic flow, and native T1 analysis using automated batch processing in the UK Biobank study. nameOfConference

  
  

  DOI: 10.1093/ehjimp/qyae094

  
  

  QMRO: qmroHref
• Shyam-Sundar V, Harding D, Khan A et al. (publicationYear). Imaging for the diagnosis of acute myocarditis: can artificial intelligence improve diagnostic performance?. nameOfConference

  
  

  DOI: 10.3389/fcvm.2024.1408574

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/98897
• Shyam-Sundar V, Mahmood A, Slabaugh G et al. (2024). Management of acute myocarditis: a systematic review of clinical practice guidelines and recommendations. nameOfConference

  
  

  DOI: 10.1093/ehjqcco/qcae069

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/99080
• Dee W, Sequeira I, Lobley A et al. (2024). Cell-vision fusion: A Swin transformer-based approach for predicting kinase inhibitor mechanism of action from Cell Painting data. nameOfConference

  
  

  DOI: 10.1016/j.isci.2024.110511

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/98917
• Senadeera DC, Yang X, Kollias D et al. (2024). CUE-Net: Violence Detection Video Analytics with Spatial Cropping, Enhanced UniformerV2 and Modified Efficient Additive Attention. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

  
  

  DOI: 10.1109/cvprw63382.2024.00493

  
  

  QMRO: qmroHref
• Jin Y, Song X, Slabaugh G et al. (2024). Partial Advantage Estimator for Proximal Policy Optimization. nameOfConference

  
  

  DOI: 10.1109/tg.2024.3408298

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/98015
• Khan A, Asad M, Benning M et al. (2024). Crop and Couple: Cardiac Image Segmentation Using Interlinked Specialist Networks. 2024 IEEE International Symposium on Biomedical Imaging (ISBI)

  
  

  DOI: 10.1109/isbi56570.2024.10635217

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/99651
• Alwazzan O, Patras I, Slabaugh G (2024). FOAA: Flattened Outer Arithmetic Attention for Multimodal Tumor Classification. nameOfConference

  
  

  DOI: 10.1109/isbi56570.2024.10635901

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/99506
• Shyam-Sundar V, Nicholls H, Harding D et al. (2024). 14 A single centre retrospective study of patients presenting with acute forms of myocarditis: insights from clinical and cardiac MRI data. ACHD/Valve disease/Pericardial disease/Cardiomyopathy

  
  

  DOI: 10.1136/heartjnl-2024-bcs.14

  
  

  QMRO: qmroHref
• Jawaid MM, Narejo S, Riaz F et al. (2024). Non-calcified plaque-based coronary stenosis grading in contrast enhanced CT. nameOfConference

  
  

  DOI: 10.1016/j.medengphy.2024.104182

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/97106
• Jaffery OA, Melki L, Slabaugh G et al. (publicationYear). A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data. nameOfConference

  
  

  DOI: 10.15420/aer.2023.25

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/97108
• Jaffery OA, Barrera CE, Rodero C et al. (2024). PO-05-163 TOWARDS AUTOMATED GENERATION OF ABLATION LESION MASKS: A UNISON OF ELECTRO AND OPTIC FLOW MAPPING. Heart Rhythm Society

  
  

  DOI: 10.1016/j.hrthm.2024.03.1434

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/97251
• Zhang Q, Zheng B, Li Z et al. (2024). Non-local degradation modeling for spatially adaptive single image super-resolution. nameOfConference

  
  

  DOI: 10.1016/j.neunet.2024.106293

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/98908
• Chen Q, Zheng B, Yan C et al. (2024). GoLDFormer: A global–local deformable window transformer for efficient image restoration. nameOfConference

  
  

  DOI: 10.1016/j.jvcir.2024.104117

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/95934
• Senior H, Slabaugh G, Yuan S et al. (2024). Graph neural networks in vision-language image understanding: a survey. nameOfConference

  
  

  DOI: 10.1007/s00371-024-03343-0

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/95988
• Papadopoulou A, Harding D, Slabaugh G et al. (2024). Prediction of atrial fibrillation and stroke using machine learning models in UK Biobank. nameOfConference

  
  

  DOI: 10.1016/j.heliyon.2024.e28034

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/95921
• Chadalavada S, Rauseo E, Salih A et al. (2024). 11 Visual quality control of assessment of AI-assisted high-volume CMR segmentation in the UK Biobank. Abstracts

  
  

  DOI: 10.1136/heartjnl-2024-bscmr.9

  
  

  QMRO: qmroHref
• Khan A, Asad M, Zolotarev A et al. (2024). Misclassification Loss for Segmentation of the Aortic Vessel Tree. nameOfConference

  
  

  DOI: 10.1007/978-3-031-53241-2_6

  
  

  QMRO: qmroHref
• Anselmi M, Slabaugh G, Crespo-Otero R et al. (2024). Molecular graph transformer: stepping beyond ALIGNN into long-range interactions. nameOfConference

  
  

  DOI: 10.1039/d4dd00014e

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/97109
• Jaffery OA, Horrach CV, Lagalante DJ et al. (2023). Subject-Specific Ablation of Pathologic Conduction Patterns Beyond the Pulmonary Veins: A Personalised Modelling Approach. 2023 Computing in Cardiology Conference (CinC)

  
  

  DOI: 10.22489/cinc.2023.400

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/94421
• Slabaugh G, Beltran L, Rizvi H et al. (publicationYear). Applications of machine and deep learning to thyroid cytology and histopathology: a review. nameOfConference

  
  

  DOI: 10.3389/fonc.2023.958310

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/94545
• Bransby KM, Slabaugh G, Bourantas C et al. (2023). Joint Dense-Point Representation for Contour-Aware Graph Segmentation. nameOfConference

  
  

  DOI: 10.1007/978-3-031-43898-1_50

  
  

  QMRO: qmroHref
• Rauseo E, Abdulkareem M, Khan A et al. (2023). Phenotyping left ventricular systolic dysfunction in asymptomatic individuals for improved risk stratification. nameOfConference

  
  

  DOI: 10.1093/ehjci/jead218

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/90354
• Chen R, Zheng B, Zhang H et al. (2023). Improving Dynamic HDR Imaging with Fusion Transformer. Annual AAAI Conference on Artificial Intelligence

  
  

  DOI: 10.1609/aaai.v37i1.25107

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/94548
• Arain Z, Iliodromiti S, Slabaugh G et al. (2023). Machine learning and disease prediction in obstetrics. nameOfConference

  
  

  DOI: 10.1016/j.crphys.2023.100099

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/88477
• Khan A, Alwazzan O, Benning M et al. (2023). Sequential Segmentation of the Left Atrium and Atrial Scars Using a Multi-scale Weight Sharing Network and Boundary-Based Processing. nameOfConference

  
  

  DOI: 10.1007/978-3-031-31778-1_7

  
  

  QMRO: qmroHref
• Bransby KM, Tufaro V, Çap M et al. (2023). 3D Coronary Vessel Reconstruction from Bi-Plane Angiography Using Graph Convolutional Networks. 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)

  
  

  DOI: 10.1109/isbi53787.2023.10230372

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/92246
• Alwazzan O, Khan A, Patras I et al. (2023). MOAB: Multi-Modal Outer Arithmetic Block for Fusion of Histopathological Images and Genetic Data for Brain Tumor Grading. 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)

  
  

  DOI: 10.1109/isbi53787.2023.10230698

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/92245
• Rauseo E, Salih A, Raisi-Estabragh Z et al. (2023). Ischemic Heart Disease and Vascular Risk Factors Are Associated With Accelerated Brain Aging. nameOfConference

  
  

  DOI: 10.1016/j.jcmg.2023.01.016

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/83601
• Fu Q, Xie H, Qin Z et al. (2023). Vector Quantized Semantic Communication System. nameOfConference

  
  

  DOI: 10.1109/lwc.2023.3255221

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/85144
• Zheng B, Pan X, Zhang H et al. (2022). DomainPlus: Cross Transform Domain Learning towards High Dynamic Range Imaging. Proceedings of the 30th ACM International Conference on Multimedia

  
  

  DOI: 10.1145/3503161.3547823

  
  

  QMRO: qmroHref
• Zheng B, Yuan S, Yan C et al. (2022). Learning Frequency Domain Priors for Image Demoireing. nameOfConference

  
  

  DOI: 10.1109/tpami.2021.3115139

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/74575
• Catley-Chandar S, Tanay T, Vandroux L et al. (2022). FlexHDR: Modelling Alignment and Exposure Uncertainties for Flexible HDR Imaging. nameOfConference

  
  

  DOI: 10.1109/TIP.2022.3203562

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/81931
• Zheng B, Chen Q, Yuan S et al. (2024). Constrained Predictive Filters for Single Image Bokeh Rendering. nameOfConference

  
  

  DOI: 10.1109/tci.2022.3171417

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/78760
• Tanay T, Sootla A, Maggioni M et al. (2022). Diagnosing and Preventing Instabilities in Recurrent Video Processing. nameOfConference

  
  

  DOI: 10.1109/TPAMI.2022.3160350

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/78095
• Sun Y, Li L, Yao T et al. (2022). Bidirectional difference locating and semantic consistency reasoning for change captioning. nameOfConference

  
  

  DOI: 10.1002/int.22821

  
  

  QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/78039
• Zhao H, Zheng B, Yuan S et al. (2022). CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhancement. nameOfConference

  
  

  DOI: 10.1109/tcsvt.2021.3123621

  
  

  QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/76440
• Moran S, McDonagh S, Slabaugh G (2021). CuRL: Neural curve layers for global image enhancement. International Conference on Pattern Recognition

  
  

  DOI: 10.1109/ICPR48806.2021.9412677

  
  

  QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/73593
• Liu L, Liu J, Yuan S et al. (2020). Wavelet-Based Dual-Branch Network for Image Demoiréing. nameOfConference

  
  

  DOI: 10.1007/978-3-030-58601-0_6

  
  

  QMRO: qmroHref
• Yuan S, Timofte R, Leonardis A et al. (2020). NTIRE 2020 Challenge on Image Demoireing: Methods and Results. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

  
  

  DOI: 10.1109/cvprw50498.2020.00238

  
  

  QMRO: qmroHref
• Zheng B, Yuan S, Slabaugh G et al. (2020). Image Demoireing with Learnable Bandpass Filters. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

  
  

  DOI: 10.1109/cvpr42600.2020.00369

  
  

  QMRO: qmroHref
• Isobe T, Li S, Jia X et al. (2020). Video Super-resolution with Temporal Group Attention. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

  
  

  DOI: 10.1109/cvpr42600.2020.00803

  
  

  QMRO: qmroHref
• Liu L, Yuan S, Liu J et al. (2020). Self-adaptively learning to Demoiré from focused and defocused image pairs. nameOfConference

  
  

  DOI: doi

  
  

  QMRO: qmroHref
• Yuan S, Timofte R, Slabaugh G et al. (2019). AIM 2019 Challenge on Image Demoireing: Dataset and Study. 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

  
  

  DOI: 10.1109/iccvw.2019.00437

  
  

  QMRO: qmroHref
• Yuan S, Timofte R, Slabaugh G et al. (2019). AIM 2019 Challenge on Image Demoireing: Methods and Results. 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

  
  

  DOI: 10.1109/iccvw.2019.00438

  
  

  QMRO: qmroHref
• Yang G, Zhuang X, Khan H et al. (2018). Left Atrial Scarring Segmentation from Delayed-Enhancement Cardiac MRI Images: A Deep Learning Approach. nameOfConference

  
  

  DOI: 10.1201/9780429441493-6

  
  

  QMRO: qmroHref
• Yang G, Zhuang X, Khan H et al. (2018). Fully automatic segmentation and objective assessment of atrial scars for long‐standing persistent atrial fibrillation patients using late gadolinium‐enhanced MRI. nameOfConference

  
  

  DOI: 10.1002/mp.12832

  
  

  QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/73541
• Yang G, Zhuang X, Khan H et al. (2017). A fully automatic deep learning method for atrial scarring segmentation from late gadolinium-enhanced MRI images. International Symposium on Biomedical Imaging

  
  

  DOI: 10.1109/ISBI.2017.7950649

  
  

  QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/73563
• Yang G, Zhuang X, Khan H et al. (2017). Differentiation of pre-ablation and post-ablation late gadolinium-enhanced cardiac MRI scans of longstanding persistent atrial fibrillation patients. SPIE Medical Imaging

  
  

  DOI: 10.1117/12.2250910

  
  

  QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/73788
• Yang G, Zhuang X, Khan H et al. (2017). Multi-atlas propagation based left atrium segmentation coupled with super-voxel based pulmonary veins delineation in late gadolinium-enhanced cardiac MRI. SPIE Medical Imaging

  
  

  DOI: 10.1117/12.2250926

  
  

  QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/73790
• Yang G, Zhuang X, Khan H et al. (2017). Segmenting atrial fibrosis from late gadolinium-enhanced cardiac MRI by deep-learned features with stacked sparse auto-encoders. SPIE Medical Imaging

  
  

  DOI: 10.1007/978-3-319-60964-5_17

  
  

  QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/73638
• Yang G, Ye X, Slabaugh G et al. (2016). Super-Resolved Enhancement of a Single Image and Its Application in Cardiac MRI. nameOfConference

  
  

  DOI: 10.1007/978-3-319-33618-3_19

  
  

  QMRO: qmroHref
• Yang G, Ye X, Slabaugh G 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

  
  

  DOI: 10.1117/12.2207440

  
  

  QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/73789
• Boone DJ, Halligan S, Roth HR et al. (2013). CT Colonography: External Clinical Validation of an Algorithm for Computer-assisted Prone and Supine Registration. nameOfConference

  
  

  DOI: 10.1148/radiol.13122083

  
  

  QMRO: qmroHref
• Ye X, Lin X, Dehmeshki J et al. (2009). Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images. nameOfConference

  
  

  DOI: 10.1109/TBME.2009.2017027

  
  

  QMRO: qmroHref

Grants

• (Knowledge base lead) Accelerated Knowledge Transfer to Innovate (AKT2I) with Keen AI, Innovate UK, 2023
• (Co-Investigator) An Ecosystem for Digital Twins in Healthcare, Horizon Europe, 2023-2025
• (Research Collaborator) Biomedical Research Centre, NIHR, 2022-2027
• (Knowledge base lead) Queen Mary University of London and Wise Plc Knowledge Transfer Partnership (KTP), 2022-2023
• (Co-Investigator) Collaborative Training Partnership in AI for Drug Discovery, led by Exscientia PLC, BBRSC, 2022-2028