Kunio Doi
Impact in
-
- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
- Medical Imaging Techniques and Applications
- Artificial Intelligence top 2%
- AI in cancer detection
Papers in
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- Radiomics and Machine Learning in Medical Imaging 8
- Medical Imaging Techniques and Applications 5
- COVID-19 diagnosis using AI 4
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- AI in cancer detection 14
- Co-authors
- Maryellen L. Giger (9 shared papers)Robert M. Nishikawa (8 shared papers)Shigehiko Katsuragawa (7 shared papers)Heber MacMahon (8 shared papers)Charles E. Metz (3 shared papers)Takayuki Ishida (4 shared papers)Robert A. Schmidt (1 shared paper)Yulei Jiang (2 shared papers)
- Journals
- Medical Physics (7 papers)Academic Radiology (4 papers)Journal of Digital Imaging (3 papers)EXPERIMENTAL ANIMALS (2 papers)Experimental and Toxicologic Pathology (2 papers)
- Partner nations
- JapanUnited StatesThailand
In The Last Decade
Kunio Doi
69 papers receiving 1.5k citations
Peers
Comparison fields: 5 of 130
- Radiology, Nuclear Medicine and Imaging 740
- Artificial Intelligence 567
- Pulmonary and Respiratory Medicine 499
- Health Informatics 15
- Computer Vision and Pattern Recognition 224
Countries citing papers authored by Kunio Doi
This map shows the geographic impact of Kunio Doi'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 Kunio Doi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kunio Doi more than expected).
Fields of papers citing papers by Kunio Doi
This network shows the impact of papers produced by Kunio Doi. 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 Kunio Doi. The network helps show where Kunio Doi may publish in the future.
Co-authors
The 25 scholars most cited alongside Kunio Doi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 74 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 1999 | 235 | |
| 2 | 2002 | 174 | |
| 3 | 1994 | 128 | |
| 4 | 2000 | 113 | |
| 5 | 1997 | 112 | |
| 6 | 1999 | 69 | |
| 7 | 1999 | 53 | |
| 8 | 1993 | 53 | |
| 9 | 1982 | 45 | |
| 10 | 1998 | 44 | |
| 11 | 2001 | 40 | |
| 12 | 1997 | 36 | |
| 13 | 1990 | 35 | |
| 14 | 1998 | 35 | |
| 15 | 1995 | 31 | |
| 16 | 2006 | 24 | |
| 17 | 1979 | 24 | |
| 18 | 2002 | 19 | |
| 19 | 2004 | 16 | |
| 20 | 1983 | 16 |
About Kunio Doi
Kunio Doi is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Pulmonary and Respiratory Medicine, Genetics and Animal Science and Zoology, having authored 74 papers that have together received 1.6k indexed citations. Recurring topics across this work include AI in cancer detection (14 papers), Virus-based gene therapy research (8 papers), Radiomics and Machine Learning in Medical Imaging (8 papers), Animal Virus Infections Studies (7 papers), Digital Radiography and Breast Imaging (5 papers), Medical Imaging Techniques and Applications (5 papers), Lung Cancer Diagnosis and Treatment (5 papers) and COVID-19 diagnosis using AI (4 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (740 citations), Artificial Intelligence (567 citations), Pulmonary and Respiratory Medicine (499 citations), Health Informatics (15 citations) and Computer Vision and Pattern Recognition (224 citations). Kunio Doi has collaborated with scholars based in Japan, United States and Thailand. Frequent co-authors include Maryellen L. Giger, Robert M. Nishikawa, Shigehiko Katsuragawa, Heber MacMahon, Charles E. Metz, Takayuki Ishida, Robert A. Schmidt, Yulei Jiang, Heber MacMahon and Kazuto Ashizawa. Their work appears in journals such as Medical Physics, Academic Radiology, Journal of Digital Imaging, EXPERIMENTAL ANIMALS and Experimental and Toxicologic Pathology.
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.