Jun Lian
- Radiation top 1%
- Advanced Radiotherapy Techniques 28
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- Medical Imaging Techniques and Applications 20
- Radiomics and Machine Learning in Medical Imaging 7
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- Medical Image Segmentation Techniques 16
- Advanced Neural Network Applications 12
- Advanced Image Processing Techniques 10
- Health Informatics top 5%
- Biophysics top 5%
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- Radiation Therapy and Dosimetry 11
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- Medical Imaging and Analysis 11
- Journals
- Medical Physics (16 papers)International Journal of Radiation Oncology*Biology*Physics (8 papers)IEEE Transactions on Medical Imaging (7 papers)
- Partner nations
- United StatesChinaSouth Korea
In The Last Decade
Jun Lian
73 papers receiving 2.0k citations
Hit Papers
Peers
Comparison fields: 5 of 134
- Radiation 434
- Radiology, Nuclear Medicine and Imaging 1.0k
- Computer Vision and Pattern Recognition 868
- Health Informatics 35
- Biophysics 94
Countries citing papers authored by Jun Lian
This map shows the geographic impact of Jun Lian'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 Jun Lian with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Lian more than expected).
Fields of papers citing papers by Jun Lian
This network shows the impact of papers produced by Jun Lian. 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 Jun Lian. The network helps show where Jun Lian may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jun Lian, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 1 | |
| 5 | 2023 | 22 | |
| 6 | 2023 | 1 | |
| 7 | 2023 | 4 | |
| 8 | 2021 | 3 | |
| 9 | 2020 | 23 | |
| 10 | Medical Image Synthesis with Context-Aware Generative Adversarial Networksbreakdown → | 2017 | 426 |
| 11 | 2016 | 11 | |
| 12 | 2015 | 18 | |
| 13 | 2013 | 2 | |
| 14 | 2013 | 6 | |
| 15 | 2012 | 54 | |
| 16 | 2012 | 16 | |
| 17 | 2010 | 18 | |
| 18 | 2010 | 18 | |
| 19 | 2004 | 59 | |
| 20 | 2004 | 13 |
About Jun Lian
Jun Lian is a scholar working on Radiation, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Otorhinolaryngology and Pulmonary and Respiratory Medicine, having authored 81 papers that have together received 2.1k indexed citations. Recurring topics across this work include Advanced Radiotherapy Techniques (28 papers), Medical Imaging Techniques and Applications (20 papers), Medical Image Segmentation Techniques (16 papers), Advanced Neural Network Applications (12 papers), Radiation Therapy and Dosimetry (11 papers), Medical Imaging and Analysis (11 papers), Advanced Image Processing Techniques (10 papers) and Radiomics and Machine Learning in Medical Imaging (7 papers). The work is most often cited by research in Radiation (434 citations), Radiology, Nuclear Medicine and Imaging (1.0k citations), Computer Vision and Pattern Recognition (868 citations), Health Informatics (35 citations) and Biophysics (94 citations). Jun Lian has collaborated with scholars based in United States, China and South Korea. Frequent co-authors include Dinggang Shen, Dong Nie, Qian Wang, Su Ruan, Roger Trullo, Caroline Petitjean, Li Wang, Yaozong Gao, Sihang Zhou and Tri Huynh. Their work appears in journals such as Medical Physics, International Journal of Radiation Oncology*Biology*Physics, IEEE Transactions on Medical Imaging, Journal of Applied Clinical Medical Physics and Annals of Surgical Oncology.
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.