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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- COVID-19 diagnosis using AI 6
- Radiomics and Machine Learning in Medical Imaging 5
- Medical Imaging Techniques and Applications 1
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- Lung Cancer Diagnosis and Treatment 5
- Digital Radiography and Breast Imaging 3
- Co-authors
- Heber MacMahon (8 shared papers)Robert A. Schmidt (5 shared papers)Maryellen L. Giger (5 shared papers)F Yin (3 shared papers)Carl J. Vyborny (3 shared papers)Charles E. Metz (3 shared papers)Shigehiko Katsuragawa (4 shared papers)Heang‐Ping Chan (3 shared papers)
- Journals
- Medical Physics (8 papers)Investigative Radiology (4 papers)Clinics in Chest Medicine (1 paper)Radiology (1 paper)Journal of Digital Imaging (1 paper)
- Partner nations
- United StatesGermany
In The Last Decade
Kunio Doi
16 papers receiving 885 citations
Peers
Comparison fields: 5 of 76
- Radiology, Nuclear Medicine and Imaging 568
- Artificial Intelligence 610
- Computer Vision and Pattern Recognition 272
- Health Informatics 13
- Pulmonary and Respiratory Medicine 302
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 20 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
| # | Work | ||
|---|---|---|---|
| 1 | 1990 | 274 | |
| 2 | 1994 | 111 | |
| 3 | 1990 | 103 | |
| 4 | 1994 | 98 | |
| 5 | 1989 | 70 | |
| 6 | 1993 | 67 | |
| 7 | 1992 | 49 | |
| 8 | Computer-aided diagnosis: development of automated schemes for quantitative analysis of radiographic images. | 1992 | 34 |
| 9 | 2000 | 31 | |
| 10 | 1991 | 25 | |
| 11 | 1991 | 19 | |
| 12 | 1988 | 18 | |
| 13 | 1996 | 17 | |
| 14 | 1990 | 13 | |
| 15 | 1994 | 6 | |
| 16 | 1989 | 4 |
About Kunio Doi
Kunio Doi is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology, having authored 16 papers that have together received 939 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (6 papers), AI in cancer detection (6 papers), Lung Cancer Diagnosis and Treatment (5 papers), Radiomics and Machine Learning in Medical Imaging (5 papers), Digital Radiography and Breast Imaging (3 papers), Photoacoustic and Ultrasonic Imaging (2 papers), Digital Imaging for Blood Diseases (1 paper) and Medical Imaging Techniques and Applications (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (568 citations), Artificial Intelligence (610 citations), Computer Vision and Pattern Recognition (272 citations), Health Informatics (13 citations) and Pulmonary and Respiratory Medicine (302 citations). Kunio Doi has collaborated with scholars based in United States and Germany. Frequent co-authors include Heber MacMahon, Robert A. Schmidt, Maryellen L. Giger, F Yin, Carl J. Vyborny, Charles E. Metz, Shigehiko Katsuragawa, Heang‐Ping Chan, Kwok L. Lam and Shigeru Sanada. Their work appears in journals such as Medical Physics, Investigative Radiology, Clinics in Chest Medicine, Radiology and Journal of Digital Imaging.
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.