Junjun He

3.4k citations
94 papers · 1.7k · 1 hit paper · h-index 19

Impact in

Papers in

Junjun He

86 papers receiving 1.7k citations

Junjun He's Hit Papers

Adaptive Pyramid Context Network for Semantic Segmentation 2019 · 314 citations
3140+2+4Years since publication100200300

Peers

Junjun He
Comparison fields: 5 of 124
  • Computer Vision and Pattern Recognition 956
  • Ophthalmology 160
  • Radiology, Nuclear Medicine and Imaging 397
  • Artificial Intelligence 535
  • Media Technology 138
Replace Huisi Wu with:
Huisi Wu China
Désiré Sidibé France
Yang Wen China
Geraldo Bráz Brazil
Rahil Garnavi Australia
Xipeng Pan China
Shujun Wang China
Qiangguo Jin China
Şaban Öztürk Türkiye
Lei Bi Australia
Junjun He relative to Huisi Wu China Huisi Wu's profile →
Citations per field
00.5×2.7×
Huisi Wu · 1×
Citations per year

Countries citing papers authored by Junjun He

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authors

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.

Border = papers with Junjun He Line = papers co-authored together Junjun He links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 94 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Adaptive Pyramid Context Network for Semantic Segmentation
Hit paper breakdown →
2019314
2 2019232
3 2020146
4 202380
5 202066
6 202165
7 202053
8 202050
9 202145
10 202239
11 202338
12 202037
13 202435
14 202334
15 202025
16 202121
17 202520
18 202420
19 202320
20 202217

About Junjun He

Junjun He is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Biomedical Engineering and Molecular Biology, having authored 94 papers that have together received 1.7k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (20 papers), Radiomics and Machine Learning in Medical Imaging (16 papers), AI in cancer detection (14 papers), Medical Image Segmentation Techniques (10 papers), Multimodal Machine Learning Applications (10 papers), Advanced Image and Video Retrieval Techniques (9 papers), COVID-19 diagnosis using AI (9 papers) and Digital Imaging for Blood Diseases (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (956 citations), Ophthalmology (160 citations), Radiology, Nuclear Medicine and Imaging (397 citations), Artificial Intelligence (535 citations) and Media Technology (138 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, Jin Ye, Lixu Gu, Xiaojiang Peng, Wenhao Wu, Cheng Li and Hongsheng Li. Their work appears in journals such as Biomedical Signal Processing and Control, IEEE Transactions on Medical Imaging, IEEE Transactions on Multimedia, Human Immunology and Medical Image Analysis.

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.

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