Greg Slabaugh

7.9k total citations · 2 hit papers
145 papers, 4.4k citations indexed

About

Greg Slabaugh is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Biomedical Engineering. According to data from OpenAlex, Greg Slabaugh has authored 145 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 76 papers in Computer Vision and Pattern Recognition, 32 papers in Radiology, Nuclear Medicine and Imaging and 25 papers in Biomedical Engineering. Recurrent topics in Greg Slabaugh's work include Medical Image Segmentation Techniques (35 papers), Advanced Vision and Imaging (21 papers) and Colorectal Cancer Screening and Detection (14 papers). Greg Slabaugh is often cited by papers focused on Medical Image Segmentation Techniques (35 papers), Advanced Vision and Imaging (21 papers) and Colorectal Cancer Screening and Detection (14 papers). Greg Slabaugh collaborates with scholars based in United Kingdom, United States and China. Greg Slabaugh's co-authors include Aleš Leonardis, Gözde Ünal, Xujiong Ye, Sarah Parisot, Rahaf Aljundi, Tinne Tuytelaars, Marc Masana, Xu Jia, Tong Fang and Guang Yang and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Gut.

In The Last Decade

Greg Slabaugh

137 papers receiving 4.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A continual learning survey: Defying forgetting in classification tasks

2021 974 citations Rahaf Aljundi, Marc Masana et al. IEEE Transactions on Pattern Analysis and Machine Intelligence profile →
DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

2017 674 citations Guang Yang, Simiao Yu et al. IEEE Transactions on Medical Imaging profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Greg Slabaugh United Kingdom	30	1.9k	1.2k	1.2k	640	439	145	4.4k
Ghassan Hamarneh Canada	39	2.7k 1.5×	1.9k 1.6×	1.8k 1.5×	1.1k 1.7×	552 1.3×	252	6.7k
Benoı̂t Macq Belgium	35	3.0k 1.6×	880 0.7×	661 0.6×	678 1.1×	227 0.5×	298	5.4k
Marc Niethammer United States	33	2.2k 1.2×	1.7k 1.4×	1.3k 1.1×	590 0.9×	320 0.7×	179	5.1k
Leo Grady United States	29	2.8k 1.5×	919 0.8×	715 0.6×	371 0.6×	308 0.7×	75	4.4k
Chenyang Xu United States	15	3.7k 2.0×	1.1k 0.9×	623 0.5×	560 0.9×	415 0.9×	50	5.1k
Andriy Myronenko United States	14	2.4k 1.3×	1.3k 1.0×	908 0.8×	715 1.1×	438 1.0×	33	4.5k
Aly A. Farag United States	30	3.4k 1.8×	1.0k 0.9×	699 0.6×	535 0.8×	426 1.0×	304	5.2k
Pierre‐Marc Jodoin Canada	29	4.1k 2.2×	1.3k 1.1×	1.5k 1.3×	603 0.9×	200 0.5×	89	5.9k
Ioannis A. Kakadiaris United States	41	3.6k 1.9×	985 0.8×	635 0.5×	870 1.4×	416 0.9×	266	6.1k
Weidong Cai Australia	38	3.0k 1.6×	2.1k 1.7×	2.4k 2.1×	522 0.8×	309 0.7×	303	7.0k

Countries citing papers authored by Greg Slabaugh

Since Specialization

Citations

This map shows the geographic impact of Greg Slabaugh's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Greg Slabaugh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Greg Slabaugh more than expected).

Fields of papers citing papers by Greg Slabaugh

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Greg Slabaugh. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Greg Slabaugh. The network helps show where Greg Slabaugh may publish in the future.

Co-authorship network of co-authors of Greg Slabaugh

This figure shows the co-authorship network connecting the top 25 collaborators of Greg Slabaugh. A scholar is included among the top collaborators of Greg Slabaugh based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Greg Slabaugh. Greg Slabaugh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Salih, Ahmed, Jackie A. Cooper, Christopher R. S. Banerji, et al.. (2025). Clinical Phenotypes in Hypertension: A Data-Driven Approach to Risk Stratification. Hypertension. 1 indexed citations

Bashir, Usman, Chao Wang, R Smillie, et al.. (2025). Deep learning for liver lesion segmentation and classification on staging CT scans of colorectal cancer patients: a multi-site technical validation study. Clinical Radiology. 85. 106914–106914.

Zheng, Bolun, et al.. (2024). GoLDFormer: A global–local deformable window transformer for efficient image restoration. Journal of Visual Communication and Image Representation. 100. 104117–104117.

Slabaugh, Greg, et al.. (2024). Molecular graph transformer: stepping beyond ALIGNN into long-range interactions. Digital Discovery. 3(5). 1048–1057. 3 indexed citations

Slabaugh, Greg, C. Anwar A. Chahal, Steffen E. Petersen, et al.. (2024). Management of acute myocarditis: a systematic review of clinical practice guidelines and recommendations. European Heart Journal - Quality of Care and Clinical Outcomes. 10(8). 658–668. 1 indexed citations

Asad, Muhammad, et al.. (2024). Crop and Couple: Cardiac Image Segmentation Using Interlinked Specialist Networks. 1–5. 2 indexed citations

Drummond, David, et al.. (2024). Artificial intelligence in respiratory care: perspectives on critical opportunities and challenges. Breathe. 20(3). 230189–230189. 2 indexed citations

Papadopoulou, Areti, Daniel Harding, Greg Slabaugh, Eirini Marouli, & Panos Deloukas. (2024). Prediction of atrial fibrillation and stroke using machine learning models in UK Biobank. Heliyon. 10(7). e28034–e28034. 11 indexed citations

Chadalavada, Sucharitha, Ahmed Salih, Hafiz Naderi, 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. PubMed. 2(3). qyae094–qyae094. 3 indexed citations

10.

Harding, Daniel, et al.. (2024). Imaging for the diagnosis of acute myocarditis: can artificial intelligence improve diagnostic performance?. Frontiers in Cardiovascular Medicine. 11. 1408574–1408574. 2 indexed citations

11.

Sun, Yaoqi, Liang Li, Bolun Zheng, et al.. (2022). Bidirectional difference locating and semantic consistency reasoning for change captioning. International Journal of Intelligent Systems. 37(5). 2969–2987. 8 indexed citations

12.

Zheng, Bolun, Shanxin Yuan, Chenggang Yan, et al.. (2021). Learning Frequency Domain Priors for Image Demoireing. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(11). 7705–7717. 47 indexed citations

13.

Zheng, Bolun, Shanxin Yuan, Hua Zhang, et al.. (2021). CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhancement. IEEE Transactions on Circuits and Systems for Video Technology. 32(7). 4138–4149. 19 indexed citations

14.

Moran, Seán & Greg Slabaugh. (2019). DIFAR: Deep Image Formation and Retouching. arXiv (Cornell University). 1 indexed citations

15.

Lange, Matthias De, Rahaf Aljundi, Marc Masana, et al.. (2019). Continual learning: A comparative study on how to defy forgetting in classification tasks.. arXiv (Cornell University). 81 indexed citations

16.

Yang, Guang, Simiao Yu, Hao Dong, et al.. (2017). DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction. IEEE Transactions on Medical Imaging. 37(6). 1310–1321. 674 indexed citations breakdown →

17.

Weyde, Tillman, et al.. (2016). Accuracy and interpretability trade-offs in machine learning applied to safer gambling. City Research Online (City University London). 11 indexed citations

18.

Asad, Muhammad, et al.. (2015). Hough Forest-based Corner Detection for Cervical Spine Radiographs. City Research Online (City University London). 183–188. 5 indexed citations

19.

Slabaugh, Greg, et al.. (2014). Photometric Stereo Reconstruction for Surface Analysis of Mucosal Tissue.. UWE Research Repository (UWE Bristol). 19–24. 2 indexed citations

20.

Roth, Holger R., Jamie R. McClelland, Darren Boone, et al.. (2010). Conformal Mapping of the Inner Colon Surface to a Cylinder for the Application of Prone to Supine Registration. Neurology India. 63(5). 803–803. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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