Etsuo Takada

656 citations
33 papers · 468 · h-index 11

Impact in

Papers in

Etsuo Takada

31 papers receiving 453 citations

Peers

Etsuo Takada
Comparison fields: 5 of 74
  • Radiology, Nuclear Medicine and Imaging 263
  • Artificial Intelligence 192
  • Pathology and Forensic Medicine 97
  • Neurology 55
  • Computer Vision and Pattern Recognition 65
Replace Fayu Liu with:
Fayu Liu China
Masoumeh Gity Iran
Lara Harrison Finland
Meihao Wang China
Katarzyna Dobruch‐Sobczak Poland
Xiaozhu Lin China
Niall Moore United Kingdom
Sheeba J. Sujit United States
Keh‐Shih Chuang Taiwan
David Laidley Canada
Etsuo Takada relative to Fayu Liu China Fayu Liu's profile →
Citations per field
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Fayu Liu · 1×
Citations per year

Countries citing papers authored by Etsuo Takada

Since Specialization
Citations

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

Fields of papers citing papers by Etsuo Takada

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Etsuo Takada, 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 Etsuo Takada Line = papers co-authored together Etsuo Takada links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 33 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200775
2 201470
3 201054
4 201353
5 200950
6 201027
7 200515
8 200914
9 201210
10 200910
11 200610
12 20139
13 20079
14 20178
15
Image stitching and computer-aided diagnosis for whole breast ultrasound image
20067
16 20077
17 20086
18 20064
19 19974
20 20064

About Etsuo Takada

Etsuo Takada is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Computer Vision and Pattern Recognition and Biomedical Engineering, having authored 33 papers that have together received 468 indexed citations. Recurring topics across this work include AI in cancer detection (19 papers), Ultrasound Imaging and Elastography (8 papers), Digital Radiography and Breast Imaging (5 papers), Ultrasound and Hyperthermia Applications (5 papers), Breast Lesions and Carcinomas (5 papers), Medical Image Segmentation Techniques (4 papers), Photoacoustic and Ultrasonic Imaging (4 papers) and Medical Imaging Techniques and Applications (3 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (263 citations), Artificial Intelligence (192 citations), Pathology and Forensic Medicine (97 citations), Neurology (55 citations) and Computer Vision and Pattern Recognition (65 citations). Etsuo Takada has collaborated with scholars based in Japan, Taiwan and South Korea. Frequent co-authors include Tokiko Endo, Ruey‐Feng Chang, Takako Morita, Takeshi Hara, Hiroshi Fujita, Daisuke Fukuoka, Kiyoka Omoto, Toshiko Hirai, Chiun‐Sheng Huang and Toshikazu Ito. Their work appears in journals such as Medical Physics, International Journal of Computer Assisted Radiology and Surgery, Ultrasound in Medicine & Biology, Sleep Medicine and Computer Methods and Programs in Biomedicine.

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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