Naoto Hayashi
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- Advanced Neuroimaging Techniques and Applications 48
- Advanced MRI Techniques and Applications 32
- Radiomics and Machine Learning in Medical Imaging 17
- Neurology top 1%
- Cognitive Neuroscience top 2%
- Functional Brain Connectivity Studies 16
- Computational Mathematics top 5%
- Psychiatry and Mental health top 2%
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- AI in cancer detection 16
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- Cerebrovascular and Carotid Artery Diseases 14
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- Medical Image Segmentation Techniques 14
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- Fetal and Pediatric Neurological Disorders 13
- Co-authors
- Kuni OhtomoOsamu AbeShigeki AokiHidemasa TakaoT. YoshikawaHarushi MoriYoshitaka MasutaniT. Masumoto
- Partner nations
- JapanUnited StatesAustria
In The Last Decade
Naoto Hayashi
169 papers receiving 4.2k citations
Peers
Comparison fields: 5 of 149
- Radiology, Nuclear Medicine and Imaging 2.2k
- Neurology 832
- Cognitive Neuroscience 793
- Computational Mathematics 20
- Psychiatry and Mental health 473
Countries citing papers authored by Naoto Hayashi
This map shows the geographic impact of Naoto Hayashi'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 Naoto Hayashi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Naoto Hayashi more than expected).
Fields of papers citing papers by Naoto Hayashi
This network shows the impact of papers produced by Naoto Hayashi. 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 Naoto Hayashi. The network helps show where Naoto Hayashi may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Naoto Hayashi, 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 | 2023 | 1 | |
| 2 | 2023 | 2 | |
| 3 | 2023 | 63 | |
| 4 | 2021 | 8 | |
| 5 | 2021 | 6 | |
| 6 | 2021 | 3 | |
| 7 | 2021 | 12 | |
| 8 | 2016 | 37 | |
| 9 | 2016 | 15 | |
| 10 | Performance Improvement in Anatomical Landmark Detection by a New Parameter Optimization Technique : Parameterization of Labeling Criterion for a Training Samples and Use of Novel Evaluation Function | 2013 | 1 |
| 11 | 2012 | 14 | |
| 12 | 2011 | 49 | |
| 13 | Shape based automated detection of pulmonary nodules with surface feature based false positive reduction | 2007 | 2 |
| 14 | 2004 | 7 | |
| 15 | 2004 | 0 | |
| 16 | 2003 | 16 | |
| 17 | 2003 | 4 | |
| 18 | 2003 | 27 | |
| 19 | 1995 | 64 | |
| 20 | 1993 | 3 |
About Naoto Hayashi
Naoto Hayashi is a scholar working on Radiology, Nuclear Medicine and Imaging, Neurology and Oral Surgery, having authored 174 papers that have together received 4.3k indexed citations. Recurring topics across this work include Advanced Neuroimaging Techniques and Applications (48 papers), Advanced MRI Techniques and Applications (32 papers), Radiomics and Machine Learning in Medical Imaging (17 papers), Functional Brain Connectivity Studies (16 papers), AI in cancer detection (16 papers), Cerebrovascular and Carotid Artery Diseases (14 papers), Medical Image Segmentation Techniques (14 papers) and Fetal and Pediatric Neurological Disorders (13 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (2.2k citations), Neurology (832 citations) and Cognitive Neuroscience (793 citations). Naoto Hayashi has collaborated with scholars based in Japan, United States and Austria. Frequent co-authors include Kuni Ohtomo, Osamu Abe, Shigeki Aoki, Hidemasa Takao, T. Yoshikawa, Harushi Mori, Yoshitaka Masutani, T. Masumoto, Akira Kunimatsu and Yukihiro Nomura. Their work appears in journals such as PLoS ONE, NeuroImage and Diabetes.
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.