Mitate Matsui
Impact in
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- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
- Medical Imaging Techniques and Applications
- Health Informatics top 5%
Papers in
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- Medical Imaging Techniques and Applications 1
- Radiomics and Machine Learning in Medical Imaging 1
- COVID-19 diagnosis using AI 1
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- Lung Cancer Diagnosis and Treatment 1
- Co-authors
- Yoshie Kodera (1 shared paper)Junji Shiraishi (1 shared paper)Shigehiko Katsuragawa (1 shared paper)Kunio Doi (1 shared paper)J Ikezoe (1 shared paper)Takeshi Kobayashi (1 shared paper)Hiroshi Fujita (1 shared paper)Tsuneo Matsumoto (1 shared paper)
- Journals
- Japanese Journal of Applied Physics (1 paper)American Journal of Roentgenology (1 paper)Japanese Journal of Radiological Technology (1 paper)
- Partner nations
- JapanUnited States
In The Last Decade
Mitate Matsui
2 papers receiving 642 citations
Mitate Matsui's Hit Papers
Peers
Comparison fields: 5 of 63
- Radiology, Nuclear Medicine and Imaging 508
- Health Informatics 25
- Computer Vision and Pattern Recognition 200
- Artificial Intelligence 251
- Pulmonary and Respiratory Medicine 232
Countries citing papers authored by Mitate Matsui
This map shows the geographic impact of Mitate Matsui'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 Mitate Matsui with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mitate Matsui more than expected).
Fields of papers citing papers by Mitate Matsui
This network shows the impact of papers produced by Mitate Matsui. 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 Mitate Matsui. The network helps show where Mitate Matsui may publish in the future.
Co-authors
The 11 scholars most cited alongside Mitate Matsui, 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 | Development of a Digital Image Database for Chest Radiographs With and Without a Lung Nodule Hit paper breakdown → | 2000 | 660 |
| 2 | 1972 | 6 | |
| 3 | 1994 | 0 |
About Mitate Matsui
Mitate Matsui is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Radiation, Geochemistry and Petrology and Biomedical Engineering, having authored 3 papers that have together received 666 indexed citations. Recurring topics across this work include Mineralogy and Gemology Studies (1 paper), Advanced Radiotherapy Techniques (1 paper), Lung Cancer Diagnosis and Treatment (1 paper), Medical Imaging Techniques and Applications (1 paper), Advanced X-ray and CT Imaging (1 paper), Radiomics and Machine Learning in Medical Imaging (1 paper) and COVID-19 diagnosis using AI (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (508 citations), Health Informatics (25 citations), Computer Vision and Pattern Recognition (200 citations), Artificial Intelligence (251 citations) and Pulmonary and Respiratory Medicine (232 citations). Mitate Matsui has collaborated with scholars based in Japan and United States. Frequent co-authors include Yoshie Kodera, Junji Shiraishi, Shigehiko Katsuragawa, Kunio Doi, J Ikezoe, Takeshi Kobayashi, Hiroshi Fujita, Tsuneo Matsumoto, Hajime Itoh and Mamoru Nakamura. Their work appears in journals such as Japanese Journal of Applied Physics, American Journal of Roentgenology and Japanese Journal of Radiological Technology.
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.