World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
36
Citations
7450
World Ranking
11070
National Ranking
702

Overview

Greg Slabaugh is affiliated with Queen Mary University of London in the United Kingdom. Their research spans multiple fields with a primary focus on Computer Science and Medicine. Within these broad areas, their work emphasizes specialized subfields such as Computer Vision and Pattern Recognition, Cardiology and Cardiovascular Medicine, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, as well as Media Technology.

The scientist's contributions cover a range of topics primarily related to image processing and machine learning methodologies. Key research topics include advanced image processing techniques, image and signal denoising methods, image enhancement techniques, and broader image processing techniques and applications. They have also explored domain adaptation and few-shot learning, multimodal machine learning applications, and advanced neural network applications.

Several recent papers illustrate the scope of their research. These include:

  • A continual learning survey: Defying forgetting in classification tasks (2021), published in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI (2020), published in Journal of Neuroscience Methods
  • Learning Frequency Domain Priors for Image Demoireing (2021), published in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Vector Quantized Semantic Communication System (2023), published in IEEE Wireless Communications Letters
  • FlexHDR: Modeling Alignment and Exposure Uncertainties for Flexible HDR Imaging (2022), published in IEEE Transactions on Image Processing

The publication venues where Greg has frequently contributed reflect interdisciplinary interests and include arXiv (Cornell University), bioRxiv (Cold Spring Harbor Laboratory), European Heart Journal, Heart Rhythm, and IEEE Transactions on Pattern Analysis and Machine Intelligence.

Collaboration plays an important role in Greg's research output. Frequent co-authors include Caroline H. Roney, Shanxin Yuan, Steffen E. Petersen, Elisa Rauseo, and Nay Aung.

Best Publications

  • A continual learning survey: Defying forgetting in classification tasks.

    Matthias Delange;Rahaf Aljundi;Marc Masana;Sarah Parisot

  • DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

    Guang Yang;Simiao Yu;Hao Dong;Greg Slabaugh

  • Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images

    Xujiong Ye;Xinyu Lin;J. Dehmeshki;G. Slabaugh

  • A survey of methods for volumetric scene reconstruction from photographs

    Greg Slabaugh;Bruce Culbertson;Tom Malzbender;Ron Schafer

  • Graph cuts segmentation using an elliptical shape prior

    G. Slabaugh;G. Unal

  • Reconstructing surfaces by volumetric regularization using radial basis functions

    Huong Quynh Dinh;G. Turk;G. Slabaugh

  • Shape-Driven Segmentation of the Arterial Wall in Intravascular Ultrasound Images

    G. Unal;S. Bucher;S. Carlier;G. Slabaugh

  • Reconstructing surfaces using anisotropic basis functions

    Huong Quynh Dinh;G. Turk;G. Slabaugh

  • DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI.

    Atif Riaz;Muhammad Asad;Eduardo Alonso;Greg Slabaugh

  • Automatic Segmentation of Polyps in Colonoscopic Narrow-Band Imaging Data

    M. Ganz;Xiaoyun Yang;G. Slabaugh

  • Automatic Detection of Bridge Deck Condition From Ground Penetrating Radar Images

    Z. W. Wang;Mengchu Zhou;G. G. Slabaugh;Jiefu Zhai

  • Fully automatic cervical vertebrae segmentation framework for X-ray images.

    S M Masudur Rahman Al Arif;Karen Knapp;Greg Slabaugh

  • Coupled PDEs for non-rigid registration and segmentation

    G. Unal;G. Slabaugh

  • Wavelet-Based Dual-Branch Network for Image Demoiréing

    Lin Liu;Lin Liu;Jianzhuang Liu;Shanxin Yuan;Gregory G. Slabaugh

  • More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning.

    Yu Liu;Sarah Parisot;Gregory G. Slabaugh;Xu Jia

  • A variational approach to problems in calibration of multiple cameras

    G. Unal;A. Yezzi;S. Soatto;G. Slabaugh

  • Ultrasound-Specific Segmentation via Decorrelation and Statistical Region-Based Active Contours

    G. Slabaugh;G. Unal;Tong Fang;M. Wels

  • Automatic graph cut segmentation of lesions in CT using mean shift superpixels

    Xujiong Ye;Gareth Beddoe;Greg Slabaugh

  • Learning Frequency Domain Priors for Image Demoireing.

    Bolun Zheng;Shanxin Yuan;Chenggang Yan;Xiang Tian

  • Deep fMRI: AN end-to-end deep network for classification of fMRI data

    Atif Riaz;Muhammad Asad;S M Masudur Rahman Al Arif;Eduardo Alonso

  • Deep De-Aliasing for Fast Compressive Sensing MRI

    Simiao Yu;Hao Dong;Guang Yang;Greg G. Slabaugh

Frequent Co-Authors

Holger R. Roth
Holger R. Roth Nvidia (United States)
David J. Hawkes
David J. Hawkes University College London
Eduardo Alonso
Eduardo Alonso City, University of London
Xiahai Zhuang
Xiahai Zhuang Fudan University
Yike Guo
Yike Guo Hong Kong Baptist University
Sebastien Ourselin
Sebastien Ourselin King's College London
Simon R. Arridge
Simon R. Arridge University College London
Marc Modat
Marc Modat King's College London
Pier Luigi Dragotti
Pier Luigi Dragotti Imperial College London
Anthony Yezzi
Anthony Yezzi Georgia Institute of Technology

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