Kenji Suzuki
- Radiology, Nuclear Medicine and Imaging top 0.2%
- Molecular Biology top 5%
- Pulmonary and Respiratory Medicine top 0.5%
- Computer Vision and Pattern Recognition top 0.5%
- Artificial Intelligence top 0.5%
- Topics
- Radiomics and Machine Learning in Medical Imaging (70 papers)AI in cancer detection (58 papers)Lung Cancer Diagnosis and Treatment (50 papers)
- Cited by
- Radiology, Nuclear Medicine and ImagingComputer Vision and Pattern RecognitionHealth Informatics
- Journals
- NatureProceedings of the National Academy of SciencesJournal of the American Chemical Society
- Partner nations
- JapanUnited StatesChina
In The Last Decade
Kenji Suzuki
496 papers receiving 12.8k citations
Hit Papers
Peers
Comparison fields: 5 of 211
- Radiology, Nuclear Medicine and Imaging 3.5k
- Molecular Biology 2.5k
- Pulmonary and Respiratory Medicine 2.3k
- Computer Vision and Pattern Recognition 2.0k
- Artificial Intelligence 2.0k
Countries citing papers authored by Kenji Suzuki
This map shows the geographic impact of Kenji Suzuki'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 Kenji Suzuki with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kenji Suzuki more than expected).
Fields of papers citing papers by Kenji Suzuki
This network shows the impact of papers produced by Kenji Suzuki. 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 Kenji Suzuki. The network helps show where Kenji Suzuki may publish in the future.
Co-authorship network of co-authors of Kenji Suzuki
This figure shows the co-authorship network connecting the top 25 collaborators of Kenji Suzuki. A scholar is included among the top collaborators of Kenji Suzuki 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 Kenji Suzuki. Kenji Suzuki is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 1 | |
| 3 | 2 | |
| 4 | 2 | |
| 5 | 10 | |
| 6 | 5 | |
| 7 | 1 | |
| 8 | 30 | |
| 9 | 67 | |
| 10 | 55 | |
| 11 | OJ-165 Coronary Artery Remodeling Evaluated by Multislice Computed Tomography(X-ray/CT/MRI/DSA2 (I) : OJ19)(Oral Presentation (Japanese)) | 1 |
| 12 | 2 | |
| 13 | 4 | |
| 14 | 3 | |
| 15 | 122 | |
| 16 | 4 | |
| 17 | 5 | |
| 18 | 3 | |
| 19 | 10 | |
| 20 | Accumulation of new quinolones in the blood of elderly patients | 2 |
About Kenji Suzuki
Kenji Suzuki is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Pulmonary and Respiratory Medicine, having authored 529 papers that have together received 13.4k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (70 papers), AI in cancer detection (58 papers) and Lung Cancer Diagnosis and Treatment (50 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (3.5k citations), Computer Vision and Pattern Recognition (2.0k citations) and Health Informatics (109 citations). Kenji Suzuki has collaborated with scholars based in Japan, United States and China. Frequent co-authors include Isao Horiba, Noboru Sugie, Lifeng He, Yuyan Chao, S. Y. Lee, Kunio Doi, Kesheng Wu, Hiroshi Kawasaki, Heber MacMahon and Hiroyuki Abé. Their work appears in journals such as Nature, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.
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.