Yoshie Kodera
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- Medical Imaging Techniques and Applications 16
- Radiation Dose and Imaging 12
- Radiomics and Machine Learning in Medical Imaging 5
- Health Informatics top 5%
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- Image and Signal Denoising Methods 5
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- Digital Radiography and Breast Imaging 25
- Artificial Intelligence top 5%
- AI in cancer detection 4
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- Advanced X-ray and CT Imaging 23
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- Advanced X-ray Imaging Techniques 4
- Co-authors
- Kunio DoiHiroshi FujitaShigehiko KatsuragawaMitate MatsuiTakeshi KobayashiJ IkezoeTsuneo MatsumotoJunji Shiraishi
- Cited by
- Radiology, Nuclear Medicine and ImagingHealth InformaticsComputer Vision and Pattern Recognition
- Journals
- Radiology (3 papers)American Journal of Roentgenology (1 paper)Physics in Medicine and Biology (1 paper)
- Partner nations
- JapanUnited StatesAustralia
In The Last Decade
Yoshie Kodera
54 papers receiving 868 citations
Hit Papers
Peers
Comparison fields: 5 of 87
- Radiology, Nuclear Medicine and Imaging 643
- Health Informatics 26
- Computer Vision and Pattern Recognition 238
- Pulmonary and Respiratory Medicine 340
- Artificial Intelligence 265
Countries citing papers authored by Yoshie Kodera
This map shows the geographic impact of Yoshie Kodera'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 Yoshie Kodera with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yoshie Kodera more than expected).
Fields of papers citing papers by Yoshie Kodera
This network shows the impact of papers produced by Yoshie Kodera. 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 Yoshie Kodera. The network helps show where Yoshie Kodera may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Yoshie Kodera, 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 | 2017 | 3 | |
| 2 | 2016 | 1 | |
| 3 | 2013 | 2 | |
| 4 | 2012 | 8 | |
| 5 | 2012 | 2 | |
| 6 | 2011 | 6 | |
| 7 | 2010 | 2 | |
| 8 | 2009 | 2 | |
| 9 | 2006 | 6 | |
| 10 | 2005 | 8 | |
| 11 | 2005 | 1 | |
| 12 | 2005 | 1 | |
| 13 | 2005 | 2 | |
| 14 | 2003 | 12 | |
| 15 | 1993 | 1 | |
| 16 | 1992 | 1 | |
| 17 | 1991 | 2 | |
| 18 | A new restoration method for medical X-ray images with optical blurs and quantum mottles | 1990 | 1 |
| 19 | 1989 | 1 | |
| 20 | 1988 | 12 |
About Yoshie Kodera
Yoshie Kodera is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Computer Vision and Pattern Recognition, having authored 59 papers that have together received 908 indexed citations. Recurring topics across this work include Digital Radiography and Breast Imaging (25 papers), Advanced X-ray and CT Imaging (23 papers), Medical Imaging Techniques and Applications (16 papers), Radiation Dose and Imaging (12 papers), Radiomics and Machine Learning in Medical Imaging (5 papers), Image and Signal Denoising Methods (5 papers), AI in cancer detection (4 papers) and Advanced X-ray Imaging Techniques (4 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (643 citations), Health Informatics (26 citations) and Computer Vision and Pattern Recognition (238 citations). Yoshie Kodera has collaborated with scholars based in Japan, United States and Australia. Frequent co-authors include Kunio Doi, Hiroshi Fujita, Shigehiko Katsuragawa, Mitate Matsui, Takeshi Kobayashi, J Ikezoe, Tsuneo Matsumoto, Junji Shiraishi, Katsuhiro Ichikawa and Heang‐Ping Chan. Their work appears in journals such as Radiology, American Journal of Roentgenology and Physics in Medicine and Biology.
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.