Guotong Xie
- Artificial Intelligence top 2%
- Radiology, Nuclear Medicine and Imaging top 5%
- Epidemiology
- Molecular Biology
- Computer Vision and Pattern Recognition top 5%
- Topics
- Topic Modeling (26 papers)Machine Learning in Healthcare (25 papers)Retinal Imaging and Analysis (23 papers)
- Journals
- Nature CommunicationsJournal of Clinical OncologySHILAP Revista de lepidopterología
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Guotong Xie
155 papers receiving 1.8k citations
Peers
Comparison fields: 5 of 145
- Artificial Intelligence 616
- Radiology, Nuclear Medicine and Imaging 413
- Epidemiology 239
- Molecular Biology 237
- Computer Vision and Pattern Recognition 173
Countries citing papers authored by Guotong Xie
This map shows the geographic impact of Guotong Xie'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 Guotong Xie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guotong Xie more than expected).
Fields of papers citing papers by Guotong Xie
This network shows the impact of papers produced by Guotong Xie. 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 Guotong Xie. The network helps show where Guotong Xie may publish in the future.
Co-authorship network of co-authors of Guotong Xie
This figure shows the co-authorship network connecting the top 25 collaborators of Guotong Xie. A scholar is included among the top collaborators of Guotong Xie 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 Guotong Xie. Guotong Xie is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 4 | |
| 5 | 4 | |
| 6 | 1 | |
| 7 | 3 | |
| 8 | 15 | |
| 9 | 7 | |
| 10 | 8 | |
| 11 | 12 | |
| 12 | 7 | |
| 13 | 5 | |
| 14 | 54 | |
| 15 | 21 | |
| 16 | 25 | |
| 17 | PASH at TREC 2020 Deep Learning Track: Dense Matching for Nested Ranking. | 1 |
| 18 | 15 | |
| 19 | Using Frequent Item Set Mining and Feature Selection Methods to Identify Interacted Risk Factors - The Atrial Fibrillation Case Study. | 2 |
| 20 | Building Structured Personal Health Records from Photographs of Printed Medical Records. | 8 |
About Guotong Xie
Guotong Xie is a scholar working on Health Information Management, Artificial Intelligence and Ophthalmology, having authored 172 papers that have together received 1.8k indexed citations. Recurring topics across this work include Topic Modeling (26 papers), Machine Learning in Healthcare (25 papers) and Retinal Imaging and Analysis (23 papers). The work is most often cited by research in Health Informatics (86 citations), Health Information Management (149 citations) and Artificial Intelligence (616 citations). Guotong Xie has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Chuanfeng Lv, Zhihong Liu, Bin Lv, Gang Hu, Xiaojun Yao, Wei Zhu, Xiang Li, Yuan Ni, Tiange Chen and Caihong Zeng. 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.