Mitsutaka Nemoto
- Radiology, Nuclear Medicine and Imaging top 10%
- Pulmonary and Respiratory Medicine
- Neurology top 10%
- Biomedical Engineering
- Artificial Intelligence top 10%
- Co-authors
- T. YoshikawaYukihiro NomuraShouhei HanaokaNaoto HayashiSoichiro MikiYoshitaka MasutaniKuni OhtomoOsamu Abe
- Topics
- AI in cancer detection (15 papers)Medical Imaging and Analysis (15 papers)Radiomics and Machine Learning in Medical Imaging (13 papers)
In The Last Decade
Mitsutaka Nemoto
48 papers receiving 518 citations
Peers
Comparison fields: 5 of 82
- Radiology, Nuclear Medicine and Imaging 202
- Pulmonary and Respiratory Medicine 158
- Neurology 131
- Biomedical Engineering 127
- Artificial Intelligence 118
Countries citing papers authored by Mitsutaka Nemoto
This map shows the geographic impact of Mitsutaka Nemoto'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 Mitsutaka Nemoto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mitsutaka Nemoto more than expected).
Fields of papers citing papers by Mitsutaka Nemoto
This network shows the impact of papers produced by Mitsutaka Nemoto. 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 Mitsutaka Nemoto. The network helps show where Mitsutaka Nemoto may publish in the future.
Co-authorship network of co-authors of Mitsutaka Nemoto
This figure shows the co-authorship network connecting the top 25 collaborators of Mitsutaka Nemoto. A scholar is included among the top collaborators of Mitsutaka Nemoto 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 Mitsutaka Nemoto. Mitsutaka Nemoto is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 2 | |
| 4 | 3 | |
| 5 | 1 | |
| 6 | 2 | |
| 7 | 38 | |
| 8 | 33 | |
| 9 | 5 | |
| 10 | 37 | |
| 11 | 15 | |
| 12 | 11 | |
| 13 | On Uncertainty of Anatomical Landmarks and Their Detectability by using Appearance Models | 1 |
| 14 | 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 | 1 |
| 15 | Automatic detection method for anatomical landmarks on the soft tissue in enhanced abdominal CT volumes | 0 |
| 16 | 2 | |
| 17 | 7 | |
| 18 | Study of input voxel size on voxel-based classifier for lesion detection | 4 |
| 19 | 7 | |
| 20 | 21 |
About Mitsutaka Nemoto
Mitsutaka Nemoto is a scholar working on Oral Surgery, Radiology, Nuclear Medicine and Imaging and Health Informatics, having authored 50 papers that have together received 527 indexed citations. Recurring topics across this work include AI in cancer detection (15 papers), Medical Imaging and Analysis (15 papers) and Radiomics and Machine Learning in Medical Imaging (13 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (202 citations), Neurology (131 citations) and Health Informatics (8 citations). Mitsutaka Nemoto has collaborated with scholars based in Japan, Sweden and Austria. Frequent co-authors include T. Yoshikawa, Yukihiro Nomura, Shouhei Hanaoka, Naoto Hayashi, Soichiro Miki, Yoshitaka Masutani, Kuni Ohtomo, Osamu Abe, Takahiro Nakao and Yuichi Kimura. Their work appears in journals such as Scientific Reports, Sensors and Physics in Medicine and Biology.
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.