Junji Ueno

557 total citations
60 papers, 346 citations indexed

About

Junji Ueno is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Junji Ueno has authored 60 papers receiving a total of 346 indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Artificial Intelligence, 7 papers in Computational Theory and Mathematics and 7 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Junji Ueno's work include Statistical and Computational Modeling (41 papers), Computational Drug Discovery Methods (7 papers) and Neural Networks and Applications (5 papers). Junji Ueno is often cited by papers focused on Statistical and Computational Modeling (41 papers), Computational Drug Discovery Methods (7 papers) and Neural Networks and Applications (5 papers). Junji Ueno collaborates with scholars based in Japan, Australia and Indonesia. Junji Ueno's co-authors include Tadashi Kondo, Shoichiro Takao, Yukitaka Nimura, Takayuki Kitasaka, Kensaku Mori, Masahiro Oda, Hiromu Nishitani, Chihiro Kondo, Tetsuya Tsujikawa and Shoji Sakiyama and has published in prestigious journals such as SHILAP Revista de lepidopterología, Medical Physics and Radiation Research.

In The Last Decade

Junji Ueno

60 papers receiving 327 citations

Peers

Junji Ueno
Comparison fields: 5 of 91
  • Artificial Intelligence 165
  • Radiology, Nuclear Medicine and Imaging 84
  • Pulmonary and Respiratory Medicine 61
  • Computer Vision and Pattern Recognition 44
  • Biomedical Engineering 30
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Citations per field, relative to Junji Ueno
Junji Ueno · 1×
Citations per year, relative to Junji Ueno
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Countries citing papers authored by Junji Ueno

Since Specialization
Citations

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

Fields of papers citing papers by Junji Ueno

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Junji Ueno

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

All Works

20 of 20 papers shown
# Work Indexed citations
1 4
2 29
3 1
4
Semi-automated organ segmentation using 3-dimensional medical imagery through sparse representation
6
5 10
6
Revised GMDH-type neural network using principal component-regression analysis
2
7
Medical image diagnosis of lung cancer by revised GMDH-type neural network using heuristic self-organization
1
8 7
9 2
10 1
11 1
12
MEDICAL IMAGE RECOGNITION OF ABDOMINAL MULTI-ORGANS BY RBF GMDH-TYPE NEURAL NETWORK
11
13
Multi-layered GMDH-type neural network self-selecting optimum neural network architecture and its application to 3-dimensional medical image recognition of blood vessels
35
14 6
15 4
16
Medical image recognition of the brain by revised GMDH-type neural network algorithm with a feedback loop
2
17 6
18 18
19
Detecting lung cancer symptoms with analogic CNN algorithms based on a constrained diffusion template
2
20
Analogic CNN algorithms in bronchogenic carcinoma analysis
3

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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