Mio Mori
Impact in
- Health Informatics top 5%
-
- Radiomics and Machine Learning in Medical Imaging
- MRI in cancer diagnosis
- COVID-19 diagnosis using AI
- Medical Imaging Techniques and Applications
Papers in
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- MRI in cancer diagnosis 9
- Medical Imaging Techniques and Applications 7
- Radiomics and Machine Learning in Medical Imaging 7
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- Breast Cancer Treatment Studies 9
- Co-authors
- Tomoyuki Fujioka (51 shared papers)Kazunori Kubota (38 shared papers)Goshi Oda (39 shared papers)Ukihide Tateishi (36 shared papers)Tsuyoshi Nakagawa (40 shared papers)Leona Katsuta (20 shared papers)Yuka Sugawara Kikuchi (9 shared papers)Yoshio Kitazume (8 shared papers)
In The Last Decade
Mio Mori
86 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 113
- Health Informatics 49
- Radiology, Nuclear Medicine and Imaging 495
- Artificial Intelligence 359
- Nuclear and High Energy Physics 99
- Neurology 56
Countries citing papers authored by Mio Mori
This map shows the geographic impact of Mio Mori'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 Mio Mori with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mio Mori more than expected).
Fields of papers citing papers by Mio Mori
This network shows the impact of papers produced by Mio Mori. 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 Mio Mori. The network helps show where Mio Mori may publish in the future.
Co-authors
The 25 scholars most cited alongside Mio Mori, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 91 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 153 | |
| 2 | 2009 | 113 | |
| 3 | 2020 | 70 | |
| 4 | 2020 | 54 | |
| 5 | 2007 | 53 | |
| 6 | 2020 | 52 | |
| 7 | Multiple strictures of the small intestine after long-term nonsteroidal anti-inflammatory drug therapy. | 1992 | 51 |
| 8 | 2020 | 42 | |
| 9 | 2020 | 41 | |
| 10 | 2019 | 41 | |
| 11 | 2022 | 34 | |
| 12 | 2022 | 33 | |
| 13 | 2020 | 33 | |
| 14 | 2004 | 33 | |
| 15 | 2002 | 30 | |
| 16 | 2004 | 29 | |
| 17 | 2020 | 27 | |
| 18 | 2019 | 25 | |
| 19 | 2021 | 24 | |
| 20 | 2022 | 23 |
About Mio Mori
Mio Mori is a scholar working on Radiology, Nuclear Medicine and Imaging, Cancer Research, Pulmonary and Respiratory Medicine, Pathology and Forensic Medicine and Computer Vision and Pattern Recognition, having authored 91 papers that have together received 1.4k indexed citations. Recurring topics across this work include Breast Cancer Treatment Studies (9 papers), MRI in cancer diagnosis (9 papers), Breast Lesions and Carcinomas (7 papers), Medical Imaging Techniques and Applications (7 papers), Radiomics and Machine Learning in Medical Imaging (7 papers), Silicon Carbide Semiconductor Technologies (7 papers), CCD and CMOS Imaging Sensors (5 papers) and AI in cancer detection (5 papers). The work is most often cited by research in Health Informatics (49 citations), Radiology, Nuclear Medicine and Imaging (495 citations), Artificial Intelligence (359 citations), Nuclear and High Energy Physics (99 citations) and Neurology (56 citations). Mio Mori has collaborated with scholars based in Japan and Sweden. Frequent co-authors include Tomoyuki Fujioka, Kazunori Kubota, Goshi Oda, Ukihide Tateishi, Tsuyoshi Nakagawa, Leona Katsuta, Yuka Sugawara Kikuchi, Yoshio Kitazume, Emi Yamaga and Toshiyuki Ishiba. Their work appears in journals such as Tomography, IEEE Transactions on Electron Devices, Breast Cancer, Magnetic Resonance in Medical Sciences and Diagnostics.
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.