Takayuki Ishida
- Radiology, Nuclear Medicine and Imaging top 1%
- Pulmonary and Respiratory Medicine top 5%
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Biomedical Engineering top 10%
- Co-authors
- Shigehiko KatsuragawaHeber MacMahonJunko OtaKazuto AshizawaKunio DoiRoger EngelmannKatsumi NakamuraYoshinori Tanabe
- Topics
- Lung Cancer Diagnosis and Treatment (22 papers)Radiomics and Machine Learning in Medical Imaging (22 papers)COVID-19 diagnosis using AI (18 papers)
- Partner nations
- JapanUnited StatesUnited Kingdom
In The Last Decade
Takayuki Ishida
94 papers receiving 1.6k citations
Peers
Comparison fields: 5 of 127
- Radiology, Nuclear Medicine and Imaging 1.0k
- Pulmonary and Respiratory Medicine 704
- Artificial Intelligence 351
- Computer Vision and Pattern Recognition 289
- Biomedical Engineering 258
Countries citing papers authored by Takayuki Ishida
This map shows the geographic impact of Takayuki Ishida'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 Takayuki Ishida with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Takayuki Ishida more than expected).
Fields of papers citing papers by Takayuki Ishida
This network shows the impact of papers produced by Takayuki Ishida. 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 Takayuki Ishida. The network helps show where Takayuki Ishida may publish in the future.
Co-authorship network of co-authors of Takayuki Ishida
This figure shows the co-authorship network connecting the top 25 collaborators of Takayuki Ishida. A scholar is included among the top collaborators of Takayuki Ishida 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 Takayuki Ishida. Takayuki Ishida is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 16 | |
| 4 | 2 | |
| 5 | 2 | |
| 6 | 23 | |
| 7 | 3 | |
| 8 | 2 | |
| 9 | 145 | |
| 10 | 1 | |
| 11 | 6 | |
| 12 | 25 | |
| 13 | 1 | |
| 14 | 0 | |
| 15 | 58 | |
| 16 | 2 | |
| 17 | 31 | |
| 18 | 13 | |
| 19 | 24 | |
| 20 | 0 |
About Takayuki Ishida
Takayuki Ishida is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Radiation, having authored 105 papers that have together received 1.6k indexed citations. Recurring topics across this work include Lung Cancer Diagnosis and Treatment (22 papers), Radiomics and Machine Learning in Medical Imaging (22 papers) and COVID-19 diagnosis using AI (18 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (1.0k citations), Radiation (213 citations) and Pulmonary and Respiratory Medicine (704 citations). Takayuki Ishida has collaborated with scholars based in Japan, United States and United Kingdom. Frequent co-authors include Shigehiko Katsuragawa, Heber MacMahon, Junko Ota, Kazuto Ashizawa, Kunio Doi, Kunio Doi, Roger Engelmann, Katsumi Nakamura, Yoshinori Tanabe and Hiroyuki Yoshida. Their work appears in journals such as Scientific Reports, Radiology and Biophysical Journal.
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.