Daiju Ueda
- Radiology, Nuclear Medicine and Imaging top 2%
- Electrical and Electronic Engineering top 5%
- Health Informatics top 0.05%
- Artificial Intelligence top 5%
- Condensed Matter Physics top 5%
- Co-authors
- Yukio MikiShannon L. WalstonAkitoshi ShimazakiHiromitsu TakagiG. KanoAkira YamamotoHiroyuki TatekawaHirotaka Takita
- Topics
- Radiomics and Machine Learning in Medical Imaging (34 papers)GaN-based semiconductor devices and materials (28 papers)Artificial Intelligence in Healthcare and Education (23 papers)
- Journals
- SHILAP Revista de lepidopterologíaApplied Physics LettersPLoS ONE
- Partner nations
- JapanUnited StatesItaly
In The Last Decade
Daiju Ueda
109 papers receiving 2.2k citations
Hit Papers
Peers
Comparison fields: 5 of 127
- Radiology, Nuclear Medicine and Imaging 838
- Electrical and Electronic Engineering 661
- Health Informatics 620
- Artificial Intelligence 378
- Condensed Matter Physics 360
Countries citing papers authored by Daiju Ueda
This map shows the geographic impact of Daiju Ueda'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 Daiju Ueda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daiju Ueda more than expected).
Fields of papers citing papers by Daiju Ueda
This network shows the impact of papers produced by Daiju Ueda. 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 Daiju Ueda. The network helps show where Daiju Ueda may publish in the future.
Co-authorship network of co-authors of Daiju Ueda
This figure shows the co-authorship network connecting the top 25 collaborators of Daiju Ueda. A scholar is included among the top collaborators of Daiju Ueda 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 Daiju Ueda. Daiju Ueda 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 | 0 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | 0 | |
| 7 | 1 | |
| 8 | 4 | |
| 9 | 27 | |
| 10 | 18 | |
| 11 | 7 | |
| 12 | 1 | |
| 13 | 32 | |
| 14 | 17 | |
| 15 | 28 | |
| 16 | 23 | |
| 17 | 14 | |
| 18 | 15 | |
| 19 | 14 | |
| 20 | 10 |
About Daiju Ueda
Daiju Ueda is a scholar working on Health Informatics, Condensed Matter Physics and Radiology, Nuclear Medicine and Imaging, having authored 123 papers that have together received 2.3k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (34 papers), GaN-based semiconductor devices and materials (28 papers) and Artificial Intelligence in Healthcare and Education (23 papers). The work is most often cited by research in Health Informatics (620 citations), Radiology, Nuclear Medicine and Imaging (838 citations) and Condensed Matter Physics (360 citations). Daiju Ueda has collaborated with scholars based in Japan, United States and Italy. Frequent co-authors include Yukio Miki, Shannon L. Walston, Akitoshi Shimazaki, Hiromitsu Takagi, G. Kano, Akira Yamamoto, Hiroyuki Tatekawa, Hirotaka Takita, Yasuhito Mitsuyama and Taro Shimono. Their work appears in journals such as SHILAP Revista de lepidopterología, Applied Physics Letters and PLoS ONE.
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.