Dong Ni
- Artificial Intelligence top 0.5%
- Radiology, Nuclear Medicine and Imaging top 0.5%
- Computer Vision and Pattern Recognition top 0.5%
- Molecular Biology top 10%
- Biomedical Engineering top 5%
- Topics
- AI in cancer detection (44 papers)Radiomics and Machine Learning in Medical Imaging (31 papers)Domain Adaptation and Few-Shot Learning (30 papers)
- Cited by
- Health InformaticsRadiology, Nuclear Medicine and ImagingComputer Vision and Pattern Recognition
- Journals
- Nature CommunicationsJournal of Clinical OncologySHILAP Revista de lepidopterología
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Dong Ni
216 papers receiving 6.7k citations
Hit Papers
Peers
Comparison fields: 5 of 179
- Artificial Intelligence 2.5k
- Radiology, Nuclear Medicine and Imaging 2.2k
- Computer Vision and Pattern Recognition 1.8k
- Molecular Biology 863
- Biomedical Engineering 854
Countries citing papers authored by Dong Ni
This map shows the geographic impact of Dong Ni'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 Dong Ni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dong Ni more than expected).
Fields of papers citing papers by Dong Ni
This network shows the impact of papers produced by Dong Ni. 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 Dong Ni. The network helps show where Dong Ni may publish in the future.
Co-authorship network of co-authors of Dong Ni
This figure shows the co-authorship network connecting the top 25 collaborators of Dong Ni. A scholar is included among the top collaborators of Dong Ni 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 Dong Ni. Dong Ni 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 | 0 | |
| 4 | 24 | |
| 5 | 33 | |
| 6 | 8 | |
| 7 | 11 | |
| 8 | 68 | |
| 9 | 27 | |
| 10 | 15 | |
| 11 | 11 | |
| 12 | 58 | |
| 13 | 14 | |
| 14 | 127 | |
| 15 | Deep Learning in Medical Ultrasound Analysis: A Reviewbreakdown → | 521 |
| 16 | 5 | |
| 17 | 124 | |
| 18 | 72 | |
| 19 | 63 | |
| 20 | 107 |
About Dong Ni
Dong Ni is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence, having authored 232 papers that have together received 6.8k indexed citations. Recurring topics across this work include AI in cancer detection (44 papers), Radiomics and Machine Learning in Medical Imaging (31 papers) and Domain Adaptation and Few-Shot Learning (30 papers). The work is most often cited by research in Health Informatics (287 citations), Radiology, Nuclear Medicine and Imaging (2.2k citations) and Computer Vision and Pattern Recognition (1.8k citations). Dong Ni has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Tianfu Wang, Baiying Lei, Siping Chen, Jing Qin, Xin Yang, Shengli Li, Pheng‐Ann Heng, Yi Wang, Jie‐Zhi Cheng and Dinggang Shen. Their work appears in journals such as Nature Communications, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.
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.