Liangqiong Qu
- Health Informatics top 5%
-
- Image Enhancement Techniques 6
- Human Pose and Action Recognition 5
- Multimodal Machine Learning Applications 5
- Medical Image Segmentation Techniques 4
- Biophysics top 5%
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- Radiomics and Machine Learning in Medical Imaging 8
- Medical Imaging Techniques and Applications 4
- Media Technology top 5%
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- Privacy-Preserving Technologies in Data 7
- AI in cancer detection 4
- Co-authors
- Yandong TangFeifei WangJiandong TianDaniel L. RubinZhuoran MaHongjie DaiRynson W. H. LauShengfeng He
- Journals
- IEEE Journal of Biomedical and Health Informatics (2 papers)IEEE Transactions on Image Processing (2 papers)Medical Image Analysis (2 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Liangqiong Qu
39 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 124
- Health Informatics 41
- Computer Vision and Pattern Recognition 542
- Biophysics 92
- Radiology, Nuclear Medicine and Imaging 347
- Media Technology 106
Countries citing papers authored by Liangqiong Qu
This map shows the geographic impact of Liangqiong Qu'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 Liangqiong Qu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Liangqiong Qu more than expected).
Fields of papers citing papers by Liangqiong Qu
This network shows the impact of papers produced by Liangqiong Qu. 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 Liangqiong Qu. The network helps show where Liangqiong Qu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Liangqiong Qu, 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 | 0 | |
| 2 | 2025 | 2 | |
| 3 | 2025 | 0 | |
| 4 | 2024 | 5 | |
| 5 | 2024 | 1 | |
| 6 | 2024 | 38 | |
| 7 | 2024 | 2 | |
| 8 | 2024 | 2 | |
| 9 | 2023 | 4 | |
| 10 | 2023 | 9 | |
| 11 | 2023 | 18 | |
| 12 | 2023 | 14 | |
| 13 | In vivo non-invasive confocal fluorescence imaging beyond 1,700 nm using superconducting nanowire single-photon detectorsbreakdown → | 2022 | 197 |
| 14 | 2022 | 21 | |
| 15 | 2021 | 62 | |
| 16 | 2021 | 18 | |
| 17 | 2020 | 95 | |
| 18 | 2019 | 179 | |
| 19 | 2019 | 10 | |
| 20 | 2017 | 52 |
About Liangqiong Qu
Liangqiong Qu is a scholar working on Health Informatics, Computer Vision and Pattern Recognition and Media Technology, having authored 43 papers that have together received 1.6k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (8 papers), Privacy-Preserving Technologies in Data (7 papers), Image Enhancement Techniques (6 papers), Human Pose and Action Recognition (5 papers), Multimodal Machine Learning Applications (5 papers), Medical Imaging Techniques and Applications (4 papers), Medical Image Segmentation Techniques (4 papers) and AI in cancer detection (4 papers). The work is most often cited by research in Health Informatics (41 citations), Computer Vision and Pattern Recognition (542 citations) and Biophysics (92 citations). Liangqiong Qu has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Yandong Tang, Feifei Wang, Jiandong Tian, Daniel L. Rubin, Zhuoran Ma, Hongjie Dai, Rynson W. H. Lau, Shengfeng He, Shuai Wang and Yuyin Zhou. Their work appears in journals such as IEEE Journal of Biomedical and Health Informatics, IEEE Transactions on Image Processing, Medical Image Analysis, Proceedings of the National Academy of Sciences and Pattern Recognition.
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.