Lie Ju
Impact in
- Ophthalmology top 5%
- Retinal and Optic Conditions
- Glaucoma and retinal disorders
- Retinal Diseases and Treatments
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- Retinal Imaging and Analysis
- Corneal surgery and disorders
Papers in
-
- Retinal Imaging and Analysis 9
- COVID-19 diagnosis using AI 3
- Corneal surgery and disorders 2
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- Digital Imaging for Blood Diseases 3
- Image Enhancement Techniques 1
- Co-authors
- Zongyuan Ge (20 shared papers)Lin Wang (9 shared papers)Xin Wang (6 shared papers)Xin Zhao (5 shared papers)Jinrong He (2 shared papers)Dwarikanath Mahapatra (3 shared papers)Tongliang Liu (1 shared paper)Quan Zhou (1 shared paper)
- Journals
- IEEE Transactions on Medical Imaging (3 papers)Computers in Biology and Medicine (2 papers)iScience (1 paper)Journal Of Big Data (1 paper)Translational Vision Science & Technology (1 paper)
- Partner nations
- AustraliaChinaUnited Arab Emirates
In The Last Decade
Lie Ju
22 papers receiving 354 citations
Peers
Comparison fields: 5 of 75
- Ophthalmology 63
- Radiology, Nuclear Medicine and Imaging 157
- Health Informatics 8
- Computer Vision and Pattern Recognition 92
- Small Animals 24
Countries citing papers authored by Lie Ju
This map shows the geographic impact of Lie Ju'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 Lie Ju with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lie Ju more than expected).
Fields of papers citing papers by Lie Ju
This network shows the impact of papers produced by Lie Ju. 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 Lie Ju. The network helps show where Lie Ju may publish in the future.
Co-authors
The 25 scholars most cited alongside Lie Ju, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 62 | |
| 2 | 2021 | 56 | |
| 3 | 2019 | 52 | |
| 4 | 2022 | 31 | |
| 5 | 2021 | 28 | |
| 6 | 2021 | 27 | |
| 7 | 2022 | 16 | |
| 8 | 2023 | 12 | |
| 9 | 2024 | 11 | |
| 10 | 2023 | 11 | |
| 11 | 2024 | 9 | |
| 12 | 2018 | 9 | |
| 13 | 2023 | 8 | |
| 14 | 2023 | 7 | |
| 15 | 2024 | 5 | |
| 16 | 2024 | 5 | |
| 17 | 2019 | 4 | |
| 18 | 2023 | 2 | |
| 19 | 2023 | 2 | |
| 20 | 2025 | 1 |
About Lie Ju
Lie Ju is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Artificial Intelligence, Ophthalmology and Small Animals, having authored 24 papers that have together received 360 indexed citations. Recurring topics across this work include Retinal Imaging and Analysis (9 papers), Digital Imaging for Blood Diseases (3 papers), COVID-19 diagnosis using AI (3 papers), Retinal and Optic Conditions (2 papers), Machine Learning and Data Classification (2 papers), Corneal surgery and disorders (2 papers), AI in cancer detection (2 papers) and Image Enhancement Techniques (1 paper). The work is most often cited by research in Ophthalmology (63 citations), Radiology, Nuclear Medicine and Imaging (157 citations), Health Informatics (8 citations), Computer Vision and Pattern Recognition (92 citations) and Small Animals (24 citations). Lie Ju has collaborated with scholars based in Australia, China and United Arab Emirates. Frequent co-authors include Zongyuan Ge, Lin Wang, Xin Wang, Xin Zhao, Jinrong He, Dwarikanath Mahapatra, Tongliang Liu, Quan Zhou, C. Paul Bonnington and Huimin Lu. Their work appears in journals such as IEEE Transactions on Medical Imaging, Computers in Biology and Medicine, iScience, Journal Of Big Data and Translational Vision Science & Technology.
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.