Karim Armanious

630 total citations
21 papers, 359 citations indexed

About

Karim Armanious is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Karim Armanious has authored 21 papers receiving a total of 359 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 6 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Artificial Intelligence. Recurrent topics in Karim Armanious's work include Generative Adversarial Networks and Image Synthesis (5 papers), Advanced Image Processing Techniques (5 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). Karim Armanious is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (5 papers), Advanced Image Processing Techniques (5 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). Karim Armanious collaborates with scholars based in Germany, United Kingdom and United States. Karim Armanious's co-authors include Bin Yang, Sergios Gatidis, Thomas Küstner, Sherif Abdulatif, Fritz Schick, Chenming Jiang, Tobias Hepp, Christian la Fougère, Konstantin Nikolaou and Gor Hakobyan and has published in prestigious journals such as Magnetic Resonance in Medicine, IEEE Transactions on Aerospace and Electronic Systems and Computerized Medical Imaging and Graphics.

In The Last Decade

Karim Armanious

21 papers receiving 354 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Karim Armanious Germany 10 204 96 86 51 45 21 359
Shipeng Xie China 10 219 1.1× 96 1.0× 167 1.9× 94 1.8× 8 0.2× 38 429
David A. Langan United States 10 233 1.1× 130 1.4× 216 2.5× 100 2.0× 64 1.4× 19 478
Nils Papenberg Germany 8 78 0.4× 363 3.8× 86 1.0× 34 0.7× 70 1.6× 16 451
Yothin Rakvongthai United States 11 267 1.3× 61 0.6× 171 2.0× 26 0.5× 9 0.2× 40 377
Boris Mailhé United States 10 157 0.8× 177 1.8× 66 0.8× 45 0.9× 21 0.5× 24 394
Rafael Verdú‐Monedero Spain 11 169 0.8× 170 1.8× 56 0.7× 16 0.3× 60 1.3× 42 366
Hugues Benoit-Cattin France 8 158 0.8× 116 1.2× 49 0.6× 35 0.7× 4 0.1× 18 330
Tian-ge Zhuang China 10 105 0.5× 214 2.2× 68 0.8× 28 0.5× 8 0.2× 38 339
Xiaotong Lu China 5 66 0.3× 304 3.2× 69 0.8× 40 0.8× 12 0.3× 9 435
Ping Cheng Taiwan 5 245 1.2× 50 0.5× 193 2.2× 26 0.5× 14 0.3× 15 385

Countries citing papers authored by Karim Armanious

Since Specialization
Citations

This map shows the geographic impact of Karim Armanious'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 Karim Armanious with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Karim Armanious more than expected).

Fields of papers citing papers by Karim Armanious

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Karim Armanious. 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 Karim Armanious. The network helps show where Karim Armanious may publish in the future.

Co-authorship network of co-authors of Karim Armanious

This figure shows the co-authorship network connecting the top 25 collaborators of Karim Armanious. A scholar is included among the top collaborators of Karim Armanious 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 Karim Armanious. Karim Armanious is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Armanious, Karim, et al.. (2023). USIS: Unsupervised Semantic Image Synthesis. Computers & Graphics. 111. 14–23. 8 indexed citations
2.
Armanious, Karim, et al.. (2022). Usis: Unsupervised Semantic Image Synthesis. SSRN Electronic Journal. 3 indexed citations
3.
Armanious, Karim, et al.. (2022). Wavelet-Based Unsupervised Label-to-Image Translation. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 1760–1764. 3 indexed citations
4.
Hepp, Tobias, Karim Armanious, Bernhard Schölkopf, et al.. (2021). Uncertainty estimation and explainability in deep learning-based age estimation of the human brain: Results from the German National Cohort MRI study. Computerized Medical Imaging and Graphics. 92. 101967–101967. 22 indexed citations
5.
Armanious, Karim, et al.. (2021). Uncertainty-Based Biological Age Estimation of Brain MRI Scans. arXiv (Cornell University). 68. 1100–1104. 1 indexed citations
6.
Braun, Alexander, et al.. (2021). SLPC: A VRNN-based approach for stochastic lidar prediction and completion in autonomous driving. 2021 29th European Signal Processing Conference (EUSIPCO). 721–725. 1 indexed citations
7.
Abdulatif, Sherif, et al.. (2021). Investigating Cross-Domain Losses for Speech Enhancement. 2021 29th European Signal Processing Conference (EUSIPCO). 411–415. 3 indexed citations
8.
Armanious, Karim, Tobias Hepp, Thomas Küstner, et al.. (2020). Independent attenuation correction of whole body [18F]FDG-PET using a deep learning approach with Generative Adversarial Networks. EJNMMI Research. 10(1). 53–53. 47 indexed citations
9.
Armanious, Karim, Thomas Küstner, Matthias Reimold, et al.. (2020). Independent brain 18F-FDG PET attenuation correction using a deep learning approach with Generative Adversarial Networks.. PubMed. 22(3). 179–186. 27 indexed citations
10.
Armanious, Karim, et al.. (2020). Unsupervised Adversarial Correction of Rigid MR Motion Artifacts. arXiv (Cornell University). 1494–1498. 15 indexed citations
11.
Abdulatif, Sherif, et al.. (2020). AeGAN: Time-Frequency Speech Denoising via Generative Adversarial Networks. arXiv (Cornell University). 451–455. 10 indexed citations
12.
Hakobyan, Gor, Karim Armanious, & Bin Yang. (2019). Interference-Aware Cognitive Radar: A Remedy to the Automotive Interference Problem. IEEE Transactions on Aerospace and Electronic Systems. 56(3). 2326–2339. 24 indexed citations
13.
Küstner, Thomas, et al.. (2019). Retrospective correction of motion‐affected MR images using deep learning frameworks. Magnetic Resonance in Medicine. 82(4). 1527–1540. 80 indexed citations
14.
Armanious, Karim, et al.. (2019). An Adversarial Super-Resolution Remedy for Radar Design Trade-offs. arXiv (Cornell University). 1–5. 23 indexed citations
15.
Abdulatif, Sherif, et al.. (2019). Towards Adversarial Denoising of Radar Micro-Doppler Signatures. arXiv (Cornell University). abs 1603 8155. 1–6. 12 indexed citations
16.
Armanious, Karim, et al.. (2019). Prediction of FDG uptake in Lung Tumors from CT Images Using Generative Adversarial Networks. 1–5. 2 indexed citations
17.
Armanious, Karim, Chenming Jiang, Sherif Abdulatif, et al.. (2019). Unsupervised Medical Image Translation Using Cycle-MedGAN. arXiv (Cornell University). 1–5. 64 indexed citations
18.
Armanious, Karim, et al.. (2019). Volumetric Surface-guided Graph-based Segmentation of Cardiac Adipose Tissues on Fat-Water MR Images. FreiDok plus (Universitätsbibliothek Freiburg). 1–5. 1 indexed citations
19.
Armanious, Karim, et al.. (2019). Spatial and Hierarchical Riemannian Dimensionality Reduction and Dictionary Learning for Segmenting Multichannel Images. FreiDok plus (Universitätsbibliothek Freiburg). 1–5. 1 indexed citations
20.
Abdulatif, Sherif, et al.. (2018). A Study of Human Body Characteristics Effect on Micro-Doppler-Based Person Identification using Deep Learning.. arXiv (Cornell University). 4 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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