Yuanhan Mo
Impact in
- General Social Sciences top 10%
- Computational and Text Analysis Methods
Papers in
-
- Medical Image Segmentation Techniques 3
- Advanced Neural Network Applications 2
- Digital Imaging for Blood Diseases 1
- Generative Adversarial Networks and Image Synthesis 1
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- Radiomics and Machine Learning in Medical Imaging 2
- Co-authors
- Georgios Kontonatsios (1 shared paper)Sophia Ananiadou (1 shared paper)Yike Guo (4 shared papers)Gregory Ligozio (1 shared paper)Thibaud Coroller (1 shared paper)Aimee Readie (1 shared paper)Yao Chen (1 shared paper)Faiz Jabbar (1 shared paper)
- Journals
- Medical Image Analysis (2 papers)Scientific Reports (1 paper)Systematic Reviews (1 paper)IEEE Transactions on Medical Imaging (1 paper)Rare & Special e-Zone (The Hong Kong University of Science and Technology) (1 paper)
- Partner nations
- United KingdomUnited StatesHong Kong
In The Last Decade
Yuanhan Mo
7 papers receiving 94 citations
Peers
Comparison fields: 5 of 60
- Health Informatics 3
- General Social Sciences 7
- Statistics, Probability and Uncertainty 11
- Artificial Intelligence 24
- Computer Vision and Pattern Recognition 15
Countries citing papers authored by Yuanhan Mo
This map shows the geographic impact of Yuanhan Mo'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 Yuanhan Mo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuanhan Mo more than expected).
Fields of papers citing papers by Yuanhan Mo
This network shows the impact of papers produced by Yuanhan Mo. 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 Yuanhan Mo. The network helps show where Yuanhan Mo may publish in the future.
Co-authors
The 25 scholars most cited alongside Yuanhan Mo, 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 | 2015 | 63 | |
| 2 | 2024 | 14 | |
| 3 | 2019 | 7 | |
| 4 | 2022 | 7 | |
| 5 | 2024 | 2 | |
| 6 | 2024 | 1 | |
| 7 | Deep Poincare Map For Robust Medical Image Segmentation. | 2017 | 1 |
| 8 | 2024 | 0 |
About Yuanhan Mo
Yuanhan Mo is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Biomedical Engineering and Surgery, having authored 8 papers that have together received 95 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Medical Imaging and Analysis (2 papers), Advanced Neural Network Applications (2 papers), Advanced X-ray and CT Imaging (1 paper), Digital Imaging for Blood Diseases (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper) and Neural Networks and Applications (1 paper). The work is most often cited by research in Health Informatics (3 citations), General Social Sciences (7 citations), Statistics, Probability and Uncertainty (11 citations), Artificial Intelligence (24 citations) and Computer Vision and Pattern Recognition (15 citations). Yuanhan Mo has collaborated with scholars based in United Kingdom, United States and Hong Kong. Frequent co-authors include Georgios Kontonatsios, Sophia Ananiadou, Yike Guo, Gregory Ligozio, Thibaud Coroller, Aimee Readie, Yao Chen, Faiz Jabbar, Bartłomiej W. Papież and Chengliang Dai. Their work appears in journals such as Medical Image Analysis, Scientific Reports, Systematic Reviews, IEEE Transactions on Medical Imaging and Rare & Special e-Zone (The Hong Kong University of Science and Technology).
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.