Tzu-An Song
- Radiology, Nuclear Medicine and Imaging top 10%
- Computer Vision and Pattern Recognition top 10%
- Biomedical Engineering
- Artificial Intelligence
- Media Technology top 10%
- Co-authors
- Joyita DuttaFan YangSamadrita Roy ChowdhuryMasoud MalekzadehQuanzheng LiGeorges El FakhriKeith A. JohnsonChi Liu
- Topics
- Medical Imaging Techniques and Applications (13 papers)Advanced Image Processing Techniques (7 papers)Radiomics and Machine Learning in Medical Imaging (4 papers)
- Cited by
- Radiology, Nuclear Medicine and ImagingComputer Vision and Pattern RecognitionMedia Technology
- Partner nations
- United StatesTaiwanIndonesia
In The Last Decade
Tzu-An Song
14 papers receiving 355 citations
Peers
Comparison fields: 5 of 64
- Radiology, Nuclear Medicine and Imaging 190
- Computer Vision and Pattern Recognition 115
- Biomedical Engineering 61
- Artificial Intelligence 60
- Media Technology 47
Countries citing papers authored by Tzu-An Song
This map shows the geographic impact of Tzu-An Song'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 Tzu-An Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tzu-An Song more than expected).
Fields of papers citing papers by Tzu-An Song
This network shows the impact of papers produced by Tzu-An Song. 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 Tzu-An Song. The network helps show where Tzu-An Song may publish in the future.
Co-authorship network of co-authors of Tzu-An Song
This figure shows the co-authorship network connecting the top 25 collaborators of Tzu-An Song. A scholar is included among the top collaborators of Tzu-An Song 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 Tzu-An Song. Tzu-An Song 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 | 0 | |
| 3 | 22 | |
| 4 | 9 | |
| 5 | 0 | |
| 6 | 57 | |
| 7 | 30 | |
| 8 | 2 | |
| 9 | 9 | |
| 10 | 58 | |
| 11 | 64 | |
| 12 | 4 | |
| 13 | 26 | |
| 14 | 69 | |
| 15 | 3 | |
| 16 | Super-resolution PET imaging using a generative adversarial network | 1 |
| 17 | 9 |
About Tzu-An Song
Tzu-An Song is a scholar working on Radiology, Nuclear Medicine and Imaging, Biophysics and Computer Vision and Pattern Recognition, having authored 17 papers that have together received 363 indexed citations. Recurring topics across this work include Medical Imaging Techniques and Applications (13 papers), Advanced Image Processing Techniques (7 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (190 citations), Computer Vision and Pattern Recognition (115 citations) and Media Technology (47 citations). Tzu-An Song has collaborated with scholars based in United States, Taiwan and Indonesia. Frequent co-authors include Joyita Dutta, Fan Yang, Samadrita Roy Chowdhury, Masoud Malekzadeh, Quanzheng Li, Georges El Fakhri, Keith A. Johnson, Chi Liu, Juan Liu and Heidi I.L. Jacobs. Their work appears in journals such as PLoS ONE, SLEEP and Physics in Medicine and Biology.
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.