Redha Ali
Impact in
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- Digital Imaging for Blood Diseases
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
- Retinal Imaging and Analysis
Papers in
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- Digital Imaging for Blood Diseases 4
- Advanced Image and Video Retrieval Techniques 3
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- Radiomics and Machine Learning in Medical Imaging 5
- COVID-19 diagnosis using AI 4
- Co-authors
- Russell C. Hardie (7 shared papers)Barath Narayanan Narayanan (5 shared papers)Temesguen M. Kebede (1 shared paper)Hailong Li (4 shared papers)Nehal A. Parikh (3 shared papers)Jonathan R. Dillman (3 shared papers)Lili He (3 shared papers)Hui Wang (1 shared paper)
- Journals
- European Radiology (1 paper)Applied Sciences (1 paper)Pediatric Radiology (1 paper)EURASIP Journal on Image and Video Processing (1 paper)Journal of Imaging Informatics in Medicine (2 papers)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Redha Ali
15 papers receiving 225 citations
Peers
Comparison fields: 5 of 60
- Computer Vision and Pattern Recognition 111
- Radiology, Nuclear Medicine and Imaging 83
- Media Technology 26
- Artificial Intelligence 87
- Biophysics 15
Countries citing papers authored by Redha Ali
This map shows the geographic impact of Redha Ali'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 Redha Ali with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Redha Ali more than expected).
Fields of papers citing papers by Redha Ali
This network shows the impact of papers produced by Redha Ali. 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 Redha Ali. The network helps show where Redha Ali may publish in the future.
Co-authors
The 22 scholars most cited alongside Redha Ali, 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 | 2019 | 52 | |
| 2 | 2022 | 40 | |
| 3 | 2019 | 32 | |
| 4 | 2019 | 23 | |
| 5 | 2022 | 17 | |
| 6 | 2018 | 17 | |
| 7 | 2017 | 16 | |
| 8 | 2020 | 12 | |
| 9 | 2020 | 8 | |
| 10 | 2021 | 5 | |
| 11 | 2024 | 4 | |
| 12 | 2019 | 4 | |
| 13 | 2025 | 1 | |
| 14 | 2024 | 1 | |
| 15 | 2019 | 1 |
About Redha Ali
Redha Ali is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Media Technology and Epidemiology, having authored 15 papers that have together received 233 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (5 papers), Digital Imaging for Blood Diseases (4 papers), COVID-19 diagnosis using AI (4 papers), Advanced Image and Video Retrieval Techniques (3 papers), AI in cancer detection (3 papers), Liver Disease Diagnosis and Treatment (2 papers), Image Processing Techniques and Applications (2 papers) and Cell Image Analysis Techniques (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (111 citations), Radiology, Nuclear Medicine and Imaging (83 citations), Media Technology (26 citations), Artificial Intelligence (87 citations) and Biophysics (15 citations). Redha Ali has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Russell C. Hardie, Barath Narayanan Narayanan, Temesguen M. Kebede, Hailong Li, Nehal A. Parikh, Jonathan R. Dillman, Lili He, Hui Wang, Mekibib Altaye and Scott B. Reeder. Their work appears in journals such as European Radiology, Applied Sciences, Pediatric Radiology, EURASIP Journal on Image and Video Processing and Journal of Imaging Informatics in Medicine.
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.