Fangzhou Liao
- Radiology, Nuclear Medicine and Imaging top 5%
- Pulmonary and Respiratory Medicine top 10%
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Biomedical Engineering
- Co-authors
- Xiaolin HuSen SongMing LiangZhe LiJianyong WeiXi ChenJun ZhuYi Shan
- Topics
- Advanced Neural Network Applications (3 papers)Radiomics and Machine Learning in Medical Imaging (2 papers)Aortic Disease and Treatment Approaches (2 papers)
- Cited by
- Radiology, Nuclear Medicine and ImagingHealth InformaticsPulmonary and Respiratory Medicine
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Fangzhou Liao
9 papers receiving 582 citations
Hit Papers
Peers
Comparison fields: 5 of 75
- Radiology, Nuclear Medicine and Imaging 351
- Pulmonary and Respiratory Medicine 306
- Artificial Intelligence 157
- Computer Vision and Pattern Recognition 121
- Biomedical Engineering 97
Countries citing papers authored by Fangzhou Liao
This map shows the geographic impact of Fangzhou Liao'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 Fangzhou Liao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fangzhou Liao more than expected).
Fields of papers citing papers by Fangzhou Liao
This network shows the impact of papers produced by Fangzhou Liao. 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 Fangzhou Liao. The network helps show where Fangzhou Liao may publish in the future.
Co-authorship network of co-authors of Fangzhou Liao
This figure shows the co-authorship network connecting the top 25 collaborators of Fangzhou Liao. A scholar is included among the top collaborators of Fangzhou Liao 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 Fangzhou Liao. Fangzhou Liao 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 | 0 | |
| 3 | 41 | |
| 4 | 32 | |
| 5 | 85 | |
| 6 | 11 | |
| 7 | Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Networkbreakdown → | 317 |
| 8 | Discovering Adversarial Examples with Momentum | 30 |
| 9 | 47 | |
| 10 | 35 |
About Fangzhou Liao
Fangzhou Liao is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine, having authored 10 papers that have together received 599 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers) and Aortic Disease and Treatment Approaches (2 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (351 citations), Health Informatics (15 citations) and Pulmonary and Respiratory Medicine (306 citations). Fangzhou Liao has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Xiaolin Hu, Sen Song, Ming Liang, Zhe Li, Jianyong Wei, Xi Chen, Jun Zhu, Yi Shan, Jie Lu and Tianyu Pang. Their work appears in journals such as Nature Communications, IEEE Access and Cell Reports.
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.