Firas Khader
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
-
- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
- MRI in cancer diagnosis
- Medical Imaging Techniques and Applications
Papers in
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- Radiomics and Machine Learning in Medical Imaging 10
- COVID-19 diagnosis using AI 5
- MRI in cancer diagnosis 3
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- AI in cancer detection 4
- Machine Learning in Healthcare 3
- Co-authors
- Daniel Truhn (17 shared papers)Jakob Nikolas Kather (14 shared papers)Sven Nebelung (15 shared papers)Christiane Kühl (13 shared papers)Gustav Müller‐Franzes (11 shared papers)Soroosh Tayebi Arasteh (8 shared papers)Tianyu Han (9 shared papers)Christoph Haarburger (7 shared papers)
- Journals
- Scientific Reports (6 papers)Radiology (3 papers)npj Digital Medicine (1 paper)Cell Reports Medicine (1 paper)Nature Protocols (1 paper)
- Partner nations
- GermanyUnited KingdomUnited States
In The Last Decade
Firas Khader
17 papers receiving 370 citations
Hit Papers
Peers
Comparison fields: 5 of 76
- Health Informatics 52
- Radiology, Nuclear Medicine and Imaging 192
- Artificial Intelligence 164
- Computer Vision and Pattern Recognition 84
- Health Information Management 15
Countries citing papers authored by Firas Khader
This map shows the geographic impact of Firas Khader'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 Firas Khader with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Firas Khader more than expected).
Fields of papers citing papers by Firas Khader
This network shows the impact of papers produced by Firas Khader. 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 Firas Khader. The network helps show where Firas Khader may publish in the future.
Co-authors
The 25 scholars most cited alongside Firas Khader, 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 | Denoising diffusion probabilistic models for 3D medical image generation Hit paper breakdown → | 2023 | 121 |
| 2 | 2023 | 79 | |
| 3 | 2023 | 39 | |
| 4 | 2023 | 31 | |
| 5 | 2024 | 27 | |
| 6 | 2023 | 21 | |
| 7 | 2024 | 17 | |
| 8 | 2023 | 12 | |
| 9 | 2023 | 8 | |
| 10 | 2022 | 6 | |
| 11 | 2022 | 4 | |
| 12 | 2022 | 3 | |
| 13 | 2023 | 2 | |
| 14 | 2024 | 2 | |
| 15 | 2024 | 2 | |
| 16 | 2025 | 1 | |
| 17 | 2025 | 1 |
About Firas Khader
Firas Khader is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Health Informatics, Biomedical Engineering and Computer Vision and Pattern Recognition, having authored 17 papers that have together received 376 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (10 papers), COVID-19 diagnosis using AI (5 papers), Artificial Intelligence in Healthcare and Education (4 papers), AI in cancer detection (4 papers), MRI in cancer diagnosis (3 papers), Machine Learning in Healthcare (3 papers), Advanced X-ray and CT Imaging (2 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). The work is most often cited by research in Health Informatics (52 citations), Radiology, Nuclear Medicine and Imaging (192 citations), Artificial Intelligence (164 citations), Computer Vision and Pattern Recognition (84 citations) and Health Information Management (15 citations). Firas Khader has collaborated with scholars based in Germany, United Kingdom and United States. Frequent co-authors include Daniel Truhn, Jakob Nikolas Kather, Sven Nebelung, Christiane Kühl, Gustav Müller‐Franzes, Soroosh Tayebi Arasteh, Tianyu Han, Christoph Haarburger, Johannes Stegmaier and Sebastian Foersch. Their work appears in journals such as Scientific Reports, Radiology, npj Digital Medicine, Cell Reports Medicine and Nature Protocols.
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.