Hidetaka Arimura

1.8k citations
123 papers · 1.3k · h-index 19

Impact in

Papers in

Hidetaka Arimura

111 papers receiving 1.2k citations

Peers

Hidetaka Arimura
Comparison fields: 5 of 88
  • Radiology, Nuclear Medicine and Imaging 819
  • Health Informatics 43
  • Radiation 181
  • Pulmonary and Respiratory Medicine 547
  • Neurology 136
Replace Soumya Ghose with:
Soumya Ghose United States
Alessandro Stefano Italy
Maria Francesca Spadea Italy
Jifke F. Veenland Netherlands
Nico Karssemeijer Netherlands
Leonard Sunwoo South Korea
Shingo Iwano Japan
Michael Perkuhn Germany
Steve Bandula United Kingdom
Sachin Jambawalikar United States
Hidetaka Arimura relative to Soumya Ghose United States Soumya Ghose's profile →
Citations per field
00.5×1.7×
Soumya Ghose · 1×
Citations per year

Countries citing papers authored by Hidetaka Arimura

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authors

The 25 scholars most cited alongside Hidetaka Arimura, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Hidetaka Arimura Line = papers co-authored together Hidetaka Arimura links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 123 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2004108
2 200588
3 201884
4 200464
5 201058
6 201856
7 200952
8 200649
9 200539
10 200839
11 202036
12 200933
13 200826
14 200226
15 201723
16 202022
17 201921
18 202019
19 202118
20 200916

About Hidetaka Arimura

Hidetaka Arimura is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Radiation, Biomedical Engineering and Artificial Intelligence, 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), Medical Imaging Techniques and Applications (32 papers), Lung Cancer Diagnosis and Treatment (29 papers), AI in cancer detection (21 papers), Advanced X-ray and CT Imaging (19 papers), Medical Image Segmentation Techniques (16 papers) and Cerebrovascular and Carotid Artery Diseases (11 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (819 citations), Health Informatics (43 citations), Radiation (181 citations), Pulmonary and Respiratory Medicine (547 citations) and Neurology (136 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, Junji Shiraishi and Kenji Suzuki. Their work appears in journals such as Medical Physics, Journal of Radiation Research, Physica Medica, International Journal of Radiation Oncology*Biology*Physics and Academic 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.

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