Hajar Emami
Impact in
- Radiation top 10%
- Advanced Radiotherapy Techniques
-
- Medical Imaging Techniques and Applications
- Radiomics and Machine Learning in Medical Imaging
- Advanced MRI Techniques and Applications
Papers in
-
- AI in cancer detection 2
-
- Generative Adversarial Networks and Image Synthesis 2
- Video Surveillance and Tracking Methods 1
- Co-authors
- Ming Dong (5 shared papers)Carri Glide‐Hurst (4 shared papers)Siamak P. Nejad‐Davarani (3 shared papers)Yinlun Huang (1 shared paper)Eric Morris (1 shared paper)Lonni Schultz (1 shared paper)Xiaoning Liu (1 shared paper)Kaamran Raahemifar (1 shared paper)
- Journals
- Medical Physics (1 paper)Computers & Chemical Engineering (1 paper)Journal of Applied Clinical Medical Physics (1 paper)International Journal of Machine Learning and Computing (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesIran
In The Last Decade
Hajar Emami
6 papers receiving 286 citations
Peers
Comparison fields: 5 of 55
- Radiation 87
- Radiology, Nuclear Medicine and Imaging 125
- Health Informatics 6
- Computer Vision and Pattern Recognition 71
- Artificial Intelligence 43
Countries citing papers authored by Hajar Emami
This map shows the geographic impact of Hajar Emami'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 Hajar Emami with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hajar Emami more than expected).
Fields of papers citing papers by Hajar Emami
This network shows the impact of papers produced by Hajar Emami. 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 Hajar Emami. The network helps show where Hajar Emami may publish in the future.
Co-authors
The 9 scholars most cited alongside Hajar Emami, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 219 | |
| 2 | 2020 | 27 | |
| 3 | 2021 | 24 | |
| 4 | 2020 | 11 | |
| 5 | 2014 | 8 | |
| 6 | Cascade-based Approach for License Plate Recognition. | 2008 | 1 |
| 7 | SA-GAN: Structure-Aware Generative Adversarial Network for Shape-Preserving Synthetic CT Generation. | 2021 | 0 |
About Hajar Emami
Hajar Emami is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology, Automotive Engineering and Control and Systems Engineering, having authored 7 papers that have together received 290 indexed citations. Recurring topics across this work include Vehicle License Plate Recognition (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers), AI in cancer detection (2 papers), Advanced Radiotherapy Techniques (1 paper), Autonomous Vehicle Technology and Safety (1 paper), Video Surveillance and Tracking Methods (1 paper), Fault Detection and Control Systems (1 paper) and Digital Rights Management and Security (1 paper). The work is most often cited by research in Radiation (87 citations), Radiology, Nuclear Medicine and Imaging (125 citations), Health Informatics (6 citations), Computer Vision and Pattern Recognition (71 citations) and Artificial Intelligence (43 citations). Hajar Emami has collaborated with scholars based in United States and Iran. Frequent co-authors include Ming Dong, Carri Glide‐Hurst, Siamak P. Nejad‐Davarani, Yinlun Huang, Eric Morris, Lonni Schultz, Xiaoning Liu, Kaamran Raahemifar and Mahmood Fathy. Their work appears in journals such as Medical Physics, Computers & Chemical Engineering, Journal of Applied Clinical Medical Physics, International Journal of Machine Learning and Computing and arXiv (Cornell University).
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.