Dandan Tu
Impact in
- Health Informatics top 10%
- Artificial Intelligence in Healthcare and Education
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- Intracranial Aneurysms: Treatment and Complications
- Traumatic Brain Injury and Neurovascular Disturbances
Papers in
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- Natural Language Processing Techniques 2
- Topic Modeling 2
- Domain Adaptation and Few-Shot Learning 1
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- Cerebrovascular and Carotid Artery Diseases 1
- Co-authors
- Xiaowu Liu (3 shared papers)Changzheng Zhang (3 shared papers)Changde Li (1 shared paper)Osamah Alwalid (1 shared paper)Yongchao Xu (1 shared paper)Jia Liu (1 shared paper)Xi Long (1 shared paper)Lixin Qin (1 shared paper)
- Journals
- IEEE Transactions on Medical Imaging (1 paper)Respiration (1 paper)Radiology (1 paper)Medical Image Analysis (1 paper)Respiratory Research (1 paper)
- Partner nations
- ChinaUnited Kingdom
In The Last Decade
Dandan Tu
6 papers receiving 178 citations
Peers
Comparison fields: 5 of 39
- Health Informatics 17
- Neurology 56
- Radiology, Nuclear Medicine and Imaging 64
- Pulmonary and Respiratory Medicine 38
- Artificial Intelligence 41
Countries citing papers authored by Dandan Tu
This map shows the geographic impact of Dandan Tu'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 Dandan Tu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dandan Tu more than expected).
Fields of papers citing papers by Dandan Tu
This network shows the impact of papers produced by Dandan Tu. 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 Dandan Tu. The network helps show where Dandan Tu may publish in the future.
Co-authors
The 25 scholars most cited alongside Dandan Tu, 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 | 2020 | 80 | |
| 2 | 2021 | 60 | |
| 3 | 2023 | 23 | |
| 4 | 2022 | 10 | |
| 5 | 2022 | 3 | |
| 6 | 2024 | 2 | |
| 7 | 2024 | 0 |
About Dandan Tu
Dandan Tu is a scholar working on Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging, Computer Networks and Communications and Neurology, having authored 7 papers that have together received 178 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (2 papers), Natural Language Processing Techniques (2 papers), Topic Modeling (2 papers), Intracranial Aneurysms: Treatment and Complications (1 paper), Cerebrovascular and Carotid Artery Diseases (1 paper), Domain Adaptation and Few-Shot Learning (1 paper), Advanced Neural Network Applications (1 paper) and COVID-19 diagnosis using AI (1 paper). The work is most often cited by research in Health Informatics (17 citations), Neurology (56 citations), Radiology, Nuclear Medicine and Imaging (64 citations), Pulmonary and Respiratory Medicine (38 citations) and Artificial Intelligence (41 citations). Dandan Tu has collaborated with scholars based in China and United Kingdom. Frequent co-authors include Xiaowu Liu, Changzheng Zhang, Changde Li, Osamah Alwalid, Yongchao Xu, Jia Liu, Xi Long, Lixin Qin, Shi Gong and Qianlan Chen. Their work appears in journals such as IEEE Transactions on Medical Imaging, Respiration, Radiology, Medical Image Analysis and Respiratory Research.
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.