Akitoshi Shimazaki

875 citations
13 papers · 625 indexed · h-index 12
Topics
Radiomics and Machine Learning in Medical Imaging (6 papers)AI in cancer detection (5 papers)COVID-19 diagnosis using AI (3 papers)
Partner nations
Japan

In The Last Decade

Akitoshi Shimazaki

13 papers receiving 604 citations

Peers

Akitoshi Shimazaki
Comparison fields: 5 of 68
  • Radiology, Nuclear Medicine and Imaging 302
  • Pulmonary and Respiratory Medicine 202
  • Biomedical Engineering 164
  • Epidemiology 135
  • Artificial Intelligence 105
Replace Guangyao Wu with:
Guangyao Wu Netherlands
Jingxu Xu China
Thomas Weikert Switzerland
Alexander Bilbily Canada
Nathaniel Swinburne United States
Jochen von Spiczak Switzerland
Christian Blüthgen Switzerland
C Martinenghi Italy
Victor А. Gombolevskiy Russia
Dana J. Lin United States
Akitoshi Shimazaki relative to Guangyao Wu Netherlands Guangyao Wu's profile →
Citations per field
00.5×
Guangyao Wu · 1×
Citations per year

Countries citing papers authored by Akitoshi Shimazaki

Since Specialization
Citations

This map shows the geographic impact of Akitoshi Shimazaki'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 Akitoshi Shimazaki with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Akitoshi Shimazaki more than expected).

Fields of papers citing papers by Akitoshi Shimazaki

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Akitoshi Shimazaki. 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 Akitoshi Shimazaki. The network helps show where Akitoshi Shimazaki may publish in the future.

Co-authorship network of co-authors of Akitoshi Shimazaki

This figure shows the co-authorship network connecting the top 25 collaborators of Akitoshi Shimazaki. A scholar is included among the top collaborators of Akitoshi Shimazaki 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 Akitoshi Shimazaki. Akitoshi Shimazaki is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

13 of 13 papers shown
#WorkIndexed citations
1 6
2 17
3 90
4 13
5 15
6 33
7 25
8 20
9 13
10 69
11 147
12 136
13 41

About Akitoshi Shimazaki

Akitoshi Shimazaki is a scholar working on Radiology, Nuclear Medicine and Imaging, Developmental Neuroscience and Pulmonary and Respiratory Medicine, having authored 13 papers that have together received 625 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (6 papers), AI in cancer detection (5 papers) and COVID-19 diagnosis using AI (3 papers). The work is most often cited by research in Health Informatics (74 citations), Radiology, Nuclear Medicine and Imaging (302 citations) and Neurology (95 citations). Akitoshi Shimazaki has collaborated with scholars based in Japan. Frequent co-authors include Yukio Miki, Daiju Ueda, Akira Yamamoto, Norihisa Nishimura, Yoshiaki Azuma, Kentaro Inui, Yoshiki Yamano, Yuki Shimahara, Takashi Honjo and Yutaka Katayama. 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.

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