Ken Asada
Impact in
- Health Informatics top 0.5%
- Artificial Intelligence in Healthcare and Education
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in
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- Artificial Intelligence in Healthcare and Education 5
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- Cancer Genomics and Diagnostics 6
- MicroRNA in disease regulation 4
- Co-authors
- Ryuji HamamotoMasaaki KomatsuSyuzo KanekoHidenori MachinoKen TakasawaAkira SakaiAi DozenKanto Shozu
- Journals
- Biomolecules (7 papers)Biomedicines (5 papers)Cancers (2 papers)Briefings in Bioinformatics (2 papers)Journal of Personalized Medicine (2 papers)
- Partner nations
- JapanUnited KingdomUnited States
In The Last Decade
Ken Asada
42 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 140
- Health Informatics 153
- Radiology, Nuclear Medicine and Imaging 335
- Cancer Research 174
- Cardiology and Cardiovascular Medicine 207
- Critical Care and Intensive Care Medicine 41
Countries citing papers authored by Ken Asada
This map shows the geographic impact of Ken Asada'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 Ken Asada with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ken Asada more than expected).
Fields of papers citing papers by Ken Asada
This network shows the impact of papers produced by Ken Asada. 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 Ken Asada. The network helps show where Ken Asada may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Ken Asada, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review Hit paper breakdown → | 2024 | 58 |
| 2 | 2024 | 3 | |
| 3 | 2023 | 6 | |
| 4 | 2023 | 9 | |
| 5 | 2022 | 12 | |
| 6 | 2022 | 7 | |
| 7 | 2022 | 9 | |
| 8 | 2022 | 17 | |
| 9 | 2022 | 21 | |
| 10 | 2021 | 36 | |
| 11 | 2020 | 7 | |
| 12 | 2020 | 34 | |
| 13 | 2020 | 18 | |
| 14 | Unsupervised Shadow Detection for Ultrasound Images by Deep Learning | 2019 | 1 |
| 15 | 2019 | 22 | |
| 16 | 2018 | 11 | |
| 17 | 2018 | 47 | |
| 18 | 2016 | 6 | |
| 19 | 2014 | 42 | |
| 20 | 2009 | 55 |
About Ken Asada
Ken Asada is a scholar working on Health Informatics, Cancer Research, Radiology, Nuclear Medicine and Imaging, Critical Care and Intensive Care Medicine and Molecular Biology, having authored 44 papers that have together received 1.3k indexed citations. Recurring topics across this work include RNA modifications and cancer (7 papers), Cancer Genomics and Diagnostics (6 papers), Epigenetics and DNA Methylation (6 papers), RNA Interference and Gene Delivery (5 papers), Radiomics and Machine Learning in Medical Imaging (5 papers), Artificial Intelligence in Healthcare and Education (5 papers), MicroRNA in disease regulation (4 papers) and Fetal and Pediatric Neurological Disorders (4 papers). The work is most often cited by research in Health Informatics (153 citations), Radiology, Nuclear Medicine and Imaging (335 citations), Cancer Research (174 citations), Cardiology and Cardiovascular Medicine (207 citations) and Critical Care and Intensive Care Medicine (41 citations). Ken Asada has collaborated with scholars based in Japan, United Kingdom and United States. Frequent co-authors include Ryuji Hamamoto, Masaaki Komatsu, Syuzo Kaneko, Hidenori Machino, Ken Takasawa, Akira Sakai, Ai Dozen, Kanto Shozu, Junko Kurokawa and Tetsushi Furukawa. Their work appears in journals such as Biomolecules, Biomedicines, Cancers, Briefings in Bioinformatics and Journal of Personalized Medicine.
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.