Jiawen Yao
- Artificial Intelligence top 2%
- Radiology, Nuclear Medicine and Imaging top 2%
- Computer Vision and Pattern Recognition top 2%
- Oncology
- Computational Mechanics top 10%
- Co-authors
- Junzhou HuangXinliang ZhuChun QiXin LiuGuoying ZhaoJitendra JonnagaddalaNicholas J. HawkinsFeiyun Zhu
- Topics
- AI in cancer detection (10 papers)Radiomics and Machine Learning in Medical Imaging (9 papers)Pancreatic and Hepatic Oncology Research (4 papers)
- Cited by
- Radiology, Nuclear Medicine and ImagingComputational MathematicsComputer Vision and Pattern Recognition
- Partner nations
- ChinaUnited StatesFinland
In The Last Decade
Jiawen Yao
34 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 122
- Artificial Intelligence 582
- Radiology, Nuclear Medicine and Imaging 532
- Computer Vision and Pattern Recognition 426
- Oncology 181
- Computational Mechanics 125
Countries citing papers authored by Jiawen Yao
This map shows the geographic impact of Jiawen Yao'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 Jiawen Yao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jiawen Yao more than expected).
Fields of papers citing papers by Jiawen Yao
This network shows the impact of papers produced by Jiawen Yao. 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 Jiawen Yao. The network helps show where Jiawen Yao may publish in the future.
Co-authorship network of co-authors of Jiawen Yao
This figure shows the co-authorship network connecting the top 25 collaborators of Jiawen Yao. A scholar is included among the top collaborators of Jiawen Yao 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 Jiawen Yao. Jiawen Yao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 4 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 0 | |
| 8 | 1 | |
| 9 | 2 | |
| 10 | 73 | |
| 11 | 36 | |
| 12 | 27 | |
| 13 | 35 | |
| 14 | Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networksbreakdown → | 264 |
| 15 | 34 | |
| 16 | 51 | |
| 17 | 139 | |
| 18 | 36 | |
| 19 | 20 | |
| 20 | Regularization Methods for Identification of Structural Damage | 2 |
About Jiawen Yao
Jiawen Yao is a scholar working on Computational Mathematics, Radiology, Nuclear Medicine and Imaging and Business and International Management, having authored 39 papers that have together received 1.2k indexed citations. Recurring topics across this work include AI in cancer detection (10 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and Pancreatic and Hepatic Oncology Research (4 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (532 citations), Computational Mathematics (13 citations) and Computer Vision and Pattern Recognition (426 citations). Jiawen Yao has collaborated with scholars based in China, United States and Finland. Frequent co-authors include Junzhou Huang, Xinliang Zhu, Chun Qi, Xin Liu, Guoying Zhao, Jitendra Jonnagaddala, Nicholas J. Hawkins, Feiyun Zhu, Le Lü and Ling Zhang. Their work appears in journals such as The Science of The Total Environment, Radiology and Clinical Cancer 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.