Saba Dadsetan
Impact in
- Media Technology top 5%
- Advanced Image Fusion Techniques
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- Image and Video Quality Assessment
- Image Enhancement Techniques
- Visual Attention and Saliency Detection
- Image and Signal Denoising Methods
- Advanced Image Processing Techniques
Papers in
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- AI in cancer detection 3
- Oncology 3
- Global Cancer Incidence and Screening 2
- Colorectal Cancer Screening and Detection 2
- Co-authors
- S. Alireza Golestaneh (1 shared paper)Kris Kitani (1 shared paper)Dooman Arefan (3 shared papers)Margarita L. Zuley (3 shared papers)Shandong Wu (4 shared papers)Jules H. Sumkin (3 shared papers)Wendie A. Berg (1 shared paper)Naira Hovakimyan (1 shared paper)
- Journals
- Pattern Recognition (1 paper)Lecture notes in computer science (1 paper)arXiv (Cornell University) (1 paper)2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Saba Dadsetan
6 papers receiving 270 citations
Saba Dadsetan's Hit Papers
Peers
Comparison fields: 5 of 45
- Media Technology 134
- Computer Vision and Pattern Recognition 204
- Industrial and Manufacturing Engineering 23
- Radiology, Nuclear Medicine and Imaging 28
- Artificial Intelligence 39
Countries citing papers authored by Saba Dadsetan
This map shows the geographic impact of Saba Dadsetan'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 Saba Dadsetan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Saba Dadsetan more than expected).
Fields of papers citing papers by Saba Dadsetan
This network shows the impact of papers produced by Saba Dadsetan. 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 Saba Dadsetan. The network helps show where Saba Dadsetan may publish in the future.
Co-authors
The 10 scholars most cited alongside Saba Dadsetan, 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 | No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency Hit paper breakdown → | 2022 | 238 |
| 2 | 2022 | 28 | |
| 3 | 2020 | 5 | |
| 4 | 2021 | 4 | |
| 5 | 2021 | 3 | |
| 6 | 2019 | 1 |
About Saba Dadsetan
Saba Dadsetan is a scholar working on Artificial Intelligence, Oncology, Media Technology, Radiology, Nuclear Medicine and Imaging and Civil and Structural Engineering, having authored 6 papers that have together received 279 indexed citations. Recurring topics across this work include AI in cancer detection (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Global Cancer Incidence and Screening (2 papers), Colorectal Cancer Screening and Detection (2 papers), Image and Video Quality Assessment (1 paper), Advanced Image Fusion Techniques (1 paper), Remote Sensing in Agriculture (1 paper) and Infrastructure Maintenance and Monitoring (1 paper). The work is most often cited by research in Media Technology (134 citations), Computer Vision and Pattern Recognition (204 citations), Industrial and Manufacturing Engineering (23 citations), Radiology, Nuclear Medicine and Imaging (28 citations) and Artificial Intelligence (39 citations). Saba Dadsetan has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include S. Alireza Golestaneh, Kris Kitani, Dooman Arefan, Margarita L. Zuley, Shandong Wu, Jules H. Sumkin, Wendie A. Berg, Naira Hovakimyan, Min Sun and Heng Huang. Their work appears in journals such as Pattern Recognition, Lecture notes in computer science, arXiv (Cornell University) and 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
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.