Bin Pu
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 5%
- Domain Adaptation and Few-Shot Learning
- AI in cancer detection
Papers in
-
- Domain Adaptation and Few-Shot Learning 9
- AI in cancer detection 7
-
- Advanced Neural Network Applications 7
- Medical Image Segmentation Techniques 3
- Co-authors
- Kenli Li (15 shared papers)Ningbo Zhu (7 shared papers)Shengli Li (10 shared papers)Jianguo Chen (6 shared papers)Yan Kang (10 shared papers)Philip S. Yu (2 shared papers)Wanli Xie (2 shared papers)Wei Wei (1 shared paper)
- Journals
- IEEE Journal of Biomedical and Health Informatics (5 papers)Information Fusion (4 papers)Neurocomputing (3 papers)Neural Computing and Applications (2 papers)Future Generation Computer Systems (2 papers)
- Partner nations
- ChinaHong KongUnited Kingdom
In The Last Decade
Bin Pu
39 papers receiving 622 citations
Bin Pu's Hit Papers
Peers
Comparison fields: 5 of 92
- Health Informatics 31
- Artificial Intelligence 271
- Computer Vision and Pattern Recognition 138
- Pediatrics, Perinatology and Child Health 112
- Transportation 37
Countries citing papers authored by Bin Pu
This map shows the geographic impact of Bin Pu'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 Bin Pu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bin Pu more than expected).
Fields of papers citing papers by Bin Pu
This network shows the impact of papers produced by Bin Pu. 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 Bin Pu. The network helps show where Bin Pu may publish in the future.
Co-authors
The 25 scholars most cited alongside Bin Pu, 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 50 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Automatic Fetal Ultrasound Standard Plane Recognition Based on Deep Learning and IIoT Hit paper breakdown → | 2021 | 157 |
| 2 | 2020 | 55 | |
| 3 | 2022 | 48 | |
| 4 | 2022 | 46 | |
| 5 | 2022 | 37 | |
| 6 | 2019 | 30 | |
| 7 | 2022 | 27 | |
| 8 | 2021 | 24 | |
| 9 | 2022 | 23 | |
| 10 | 2020 | 21 | |
| 11 | 2022 | 18 | |
| 12 | 2023 | 17 | |
| 13 | 2022 | 13 | |
| 14 | 2024 | 11 | |
| 15 | 2022 | 11 | |
| 16 | 2023 | 10 | |
| 17 | 2017 | 10 | |
| 18 | 2020 | 10 | |
| 19 | 2023 | 9 | |
| 20 | 2022 | 7 |
About Bin Pu
Bin Pu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Pediatrics, Perinatology and Child Health, Epidemiology and Computer Networks and Communications, having authored 50 papers that have together received 638 indexed citations. Recurring topics across this work include Fetal and Pediatric Neurological Disorders (11 papers), Domain Adaptation and Few-Shot Learning (9 papers), Advanced Neural Network Applications (7 papers), AI in cancer detection (7 papers), Neonatal and fetal brain pathology (4 papers), Artificial Intelligence in Healthcare (4 papers), Congenital Heart Disease Studies (3 papers) and Medical Image Segmentation Techniques (3 papers). The work is most often cited by research in Health Informatics (31 citations), Artificial Intelligence (271 citations), Computer Vision and Pattern Recognition (138 citations), Pediatrics, Perinatology and Child Health (112 citations) and Transportation (37 citations). Bin Pu has collaborated with scholars based in China, Hong Kong and United Kingdom. Frequent co-authors include Kenli Li, Ningbo Zhu, Shengli Li, Jianguo Chen, Yan Kang, Philip S. Yu, Wanli Xie, Wei Wei, Haining Wang and Xiangke Liao. Their work appears in journals such as IEEE Journal of Biomedical and Health Informatics, Information Fusion, Neurocomputing, Neural Computing and Applications and Future Generation Computer Systems.
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.