Jiong Wu
- Molecular Biology
- Cellular and Molecular Neuroscience top 5%
- Computer Vision and Pattern Recognition top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Neurology top 5%
- Co-authors
- Xiaoying TangYue ZhangJohn R. SedorJames P. BasilionAmitava MukherjeeHui MiaoAaron PettyDavid Danielpour
- Topics
- Medical Image Segmentation Techniques (22 papers)Advanced Neural Network Applications (13 papers)Brain Tumor Detection and Classification (9 papers)
- Journals
- NeuronCancer CellCancer
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Jiong Wu
57 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 136
- Molecular Biology 523
- Cellular and Molecular Neuroscience 461
- Computer Vision and Pattern Recognition 252
- Radiology, Nuclear Medicine and Imaging 214
- Neurology 183
Countries citing papers authored by Jiong Wu
This map shows the geographic impact of Jiong Wu'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 Jiong Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jiong Wu more than expected).
Fields of papers citing papers by Jiong Wu
This network shows the impact of papers produced by Jiong Wu. 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 Jiong Wu. The network helps show where Jiong Wu may publish in the future.
Co-authorship network of co-authors of Jiong Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Jiong Wu. A scholar is included among the top collaborators of Jiong Wu 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 Jiong Wu. Jiong Wu 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 | 1 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 0 | |
| 7 | 0 | |
| 8 | 5 | |
| 9 | 1 | |
| 10 | 59 | |
| 11 | 35 | |
| 12 | 11 | |
| 13 | 21 | |
| 14 | 14 | |
| 15 | 42 | |
| 16 | 2 | |
| 17 | Preparation and Technology of Flame Retardant Reinforced Toughening Nylon 66 Composites | 1 |
| 18 | Depth estimation of single image based on multi-scale texture energy measure | 1 |
| 19 | 412 | |
| 20 | 251 |
About Jiong Wu
Jiong Wu is a scholar working on Neurology, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging, having authored 65 papers that have together received 1.5k indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (22 papers), Advanced Neural Network Applications (13 papers) and Brain Tumor Detection and Classification (9 papers). The work is most often cited by research in Cellular and Molecular Neuroscience (461 citations), Neurology (183 citations) and Computer Vision and Pattern Recognition (252 citations). Jiong Wu has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Xiaoying Tang, Yue Zhang, John R. Sedor, James P. Basilion, Amitava Mukherjee, Hui Miao, Aaron Petty, David Danielpour, Da‐Qiang Li and Mark L. Cohen. Their work appears in journals such as Neuron, Cancer Cell and Cancer.
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.