Junjun He
Impact in
-
- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Multimodal Machine Learning Applications
- Digital Imaging for Blood Diseases
- Ophthalmology top 5%
- Glaucoma and retinal disorders
Papers in
-
- Advanced Neural Network Applications 13
- Digital Imaging for Blood Diseases 8
- Multimodal Machine Learning Applications 8
-
- Radiomics and Machine Learning in Medical Imaging 11
- Retinal Imaging and Analysis 9
Junjun He
64 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 111
- Computer Vision and Pattern Recognition 638
- Ophthalmology 162
- Radiology, Nuclear Medicine and Imaging 313
- Media Technology 107
- Health Informatics 16
Countries citing papers authored by Junjun He
This map shows the geographic impact of Junjun He'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 Junjun He with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junjun He more than expected).
Fields of papers citing papers by Junjun He
This network shows the impact of papers produced by Junjun He. 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 Junjun He. The network helps show where Junjun He may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Junjun He, 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 | 2025 | 1 | |
| 3 | 2025 | 3 | |
| 4 | 2025 | 1 | |
| 5 | 2025 | 0 | |
| 6 | 2025 | 0 | |
| 7 | 2025 | 0 | |
| 8 | 2025 | 1 | |
| 9 | 2025 | 2 | |
| 10 | 2024 | 11 | |
| 11 | 2024 | 4 | |
| 12 | 2024 | 0 | |
| 13 | 2024 | 2 | |
| 14 | 2024 | 0 | |
| 15 | 2023 | 30 | |
| 16 | 2023 | 13 | |
| 17 | 2022 | 6 | |
| 18 | 2022 | 9 | |
| 19 | W-net: Bridged U-net for 2D Medical Image Segmentation. | 2018 | 14 |
| 20 | 2014 | 1 |
About Junjun He
Junjun He is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Ophthalmology, Artificial Intelligence and Immunology, having authored 75 papers that have together received 1.1k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (13 papers), Radiomics and Machine Learning in Medical Imaging (11 papers), Retinal Imaging and Analysis (9 papers), AI in cancer detection (9 papers), Digital Imaging for Blood Diseases (8 papers), Multimodal Machine Learning Applications (8 papers), Immune Cell Function and Interaction (7 papers) and T-cell and B-cell Immunology (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (638 citations), Ophthalmology (162 citations), Radiology, Nuclear Medicine and Imaging (313 citations), Media Technology (107 citations) and Health Informatics (16 citations). Junjun He has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Yu Qiao, Zhongying Deng, Yali Wang, Lei Zhou, Lixu Gu, Cheng Li, Ye Jin, Fei Li, Xiulan Zhang and Bin Fu. Their work appears in journals such as Biomedical Signal Processing and Control, IEEE Transactions on Medical Imaging, IEEE Transactions on Multimedia, Medical Image Analysis and Neurocomputing.
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.