Greg Slabaugh

7.9k citations
145 papers · 4.4k indexed · 2 hit papers · h-index 30

Greg Slabaugh

137 papers receiving 4.2k citations

Hit Papers

A continual learning survey: Defying forgetting ...9742017202620202023250500750

Peers

Greg Slabaugh
Comparison fields: 5 of 150
  • Computer Vision and Pattern Recognition 1.9k
  • Computer Graphics and Computer-Aided Design 262
  • Radiology, Nuclear Medicine and Imaging 1.2k
  • Artificial Intelligence 1.2k
  • Media Technology 267
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Citations per year

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

The 25 scholars most cited alongside Greg Slabaugh, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Greg Slabaugh Line = papers co-authored together Greg Slabaugh links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20251
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8 202411
9 20243
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11 20228
12 202147
13 202119
14
DIFAR: Deep Image Formation and Retouching
20191
15
Continual learning: A comparative study on how to defy forgetting in classification tasks.
201981
16
DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstructionbreakdown →
2017674
17
Accuracy and interpretability trade-offs in machine learning applied to safer gambling
201611
18
Hough Forest-based Corner Detection for Cervical Spine Radiographs
20155
19
Photometric Stereo Reconstruction for Surface Analysis of Mucosal Tissue.
20142
20 20101

About Greg Slabaugh

Greg Slabaugh is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design, Computational Mathematics, Radiology, Nuclear Medicine and Imaging and Media Technology, having authored 145 papers that have together received 4.4k indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (35 papers), Advanced Vision and Imaging (21 papers), Colorectal Cancer Screening and Detection (14 papers), Advanced Image Processing Techniques (14 papers), Image and Signal Denoising Methods (12 papers), Computer Graphics and Visualization Techniques (10 papers), Radiomics and Machine Learning in Medical Imaging (10 papers) and Image Enhancement Techniques (10 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.9k citations), Computer Graphics and Computer-Aided Design (262 citations), Radiology, Nuclear Medicine and Imaging (1.2k citations), Artificial Intelligence (1.2k citations) and Media Technology (267 citations). Greg Slabaugh has collaborated with scholars based in United Kingdom, United States and China. Frequent co-authors include Aleš Leonardis, Gözde Ünal, Xujiong Ye, Sarah Parisot, Rahaf Aljundi, Tinne Tuytelaars, Marc Masana, Xu Jia, Tong Fang and Guang Yang. Their work appears in journals such as IEEE Transactions on Biomedical Engineering, IEEE Transactions on Pattern Analysis and Machine Intelligence, Medical Physics, IEEE Signal Processing Magazine and Computer-Aided Design.

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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