Hidetaka Arimura
- Radiology, Nuclear Medicine and Imaging top 2%
- Pulmonary and Respiratory Medicine top 5%
- Artificial Intelligence top 5%
- Biomedical Engineering
- Computer Vision and Pattern Recognition top 5%
- Co-authors
- Kunio DoiKenta NinomiyaShigehiko KatsuragawaMazen SoufiYukunori KorogiYoshiyuki ShioyamaYasuo YamashitaTaiki Magome
- Topics
- Radiomics and Machine Learning in Medical Imaging (44 papers)Advanced Radiotherapy Techniques (39 papers)Medical Imaging Techniques and Applications (32 papers)
- Journals
- PLoS ONEScientific ReportsRadiology
- Partner nations
- JapanUnited StatesBelarus
In The Last Decade
Hidetaka Arimura
111 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 88
- Radiology, Nuclear Medicine and Imaging 819
- Pulmonary and Respiratory Medicine 547
- Artificial Intelligence 245
- Biomedical Engineering 223
- Computer Vision and Pattern Recognition 185
Countries citing papers authored by Hidetaka Arimura
This map shows the geographic impact of Hidetaka Arimura'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 Hidetaka Arimura with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hidetaka Arimura more than expected).
Fields of papers citing papers by Hidetaka Arimura
This network shows the impact of papers produced by Hidetaka Arimura. 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 Hidetaka Arimura. The network helps show where Hidetaka Arimura may publish in the future.
Co-authorship network of co-authors of Hidetaka Arimura
This figure shows the co-authorship network connecting the top 25 collaborators of Hidetaka Arimura. A scholar is included among the top collaborators of Hidetaka Arimura 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 Hidetaka Arimura. Hidetaka Arimura 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 | 7 | |
| 4 | 4 | |
| 5 | 2 | |
| 6 | 5 | |
| 7 | 21 | |
| 8 | 4 | |
| 9 | 1 | |
| 10 | 3 | |
| 11 | 2 | |
| 12 | 5 | |
| 13 | 13 | |
| 14 | Automated method for segmentation of white matter and gray matter regions with multiple sclerosis in 3T MR images(International Forum on Medical Imaging in Asia 2009 (IFMIA 2009)) | 1 |
| 15 | 16 | |
| 16 | 49 | |
| 17 | 88 | |
| 18 | 39 | |
| 19 | 0 | |
| 20 | Wiener spectra of quantum mottle and structure mottle condidering the effects of crossover exposure of front and back emulsions | 1 |
About Hidetaka Arimura
Hidetaka Arimura is a scholar working on Radiation, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine, having authored 123 papers that have together received 1.3k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (44 papers), Advanced Radiotherapy Techniques (39 papers) and Medical Imaging Techniques and Applications (32 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (819 citations), Health Informatics (43 citations) and Radiation (181 citations). Hidetaka Arimura has collaborated with scholars based in Japan, United States and Belarus. Frequent co-authors include Kunio Doi, Kenta Ninomiya, Shigehiko Katsuragawa, Mazen Soufi, Yukunori Korogi, Yoshiyuki Shioyama, Yasuo Yamashita, Taiki Magome, Shusuke Sone and Kenji Suzuki. Their work appears in journals such as PLoS ONE, Scientific Reports and Radiology.
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.