Sheng Li

84 papers receiving 764 citations

Peers

Sheng Li
Comparison fields: 5 of 96
  • Computer Vision and Pattern Recognition 215
  • Acoustics and Ultrasonics 8
  • Signal Processing 89
  • Computational Mechanics 144
  • Artificial Intelligence 193
Replace Alfredo Gardel with:
Alfredo Gardel Spain
Takao Onoye Japan
Wen-Chung Kao Taiwan
Rajesh Mehra India
Stamatios Georgoulis Switzerland
Hao Shen Germany
İlker Bayram Türkiye
Fei Xie China
James R. Hopgood United Kingdom
Sheng Li relative to Alfredo Gardel Spain Alfredo Gardel's profile →
Citations per field
00.5×1.5×2.1×
Alfredo Gardel · 1×
Citations per year

Countries citing papers authored by Sheng Li

Since Specialization
Citations

This map shows the geographic impact of Sheng Li'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 Sheng Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sheng Li more than expected).

Fields of papers citing papers by Sheng Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sheng Li. 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 Sheng Li. The network helps show where Sheng Li may publish in the future.

Co-authors

The 25 scholars most cited alongside Sheng Li, 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 Sheng Li Line = papers co-authored together Sheng Li links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 103 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019106
2 201549
3 202145
4 201037
5 202132
6 202128
7 201628
8 202328
9 201627
10 202023
11 202222
12 200621
13 201918
14 201917
15 202017
16 201716
17 202214
18 202014
19 201313
20 202312

About Sheng Li

Sheng Li is a scholar working on Signal Processing, Computer Vision and Pattern Recognition, Gastroenterology, Acoustics and Ultrasonics and Computational Mechanics, having authored 103 papers that have together received 790 indexed citations. Recurring topics across this work include Hydraulic and Pneumatic Systems (17 papers), Sparse and Compressive Sensing Techniques (17 papers), Blind Source Separation Techniques (10 papers), Colorectal Cancer Screening and Detection (9 papers), Advanced Clustering Algorithms Research (9 papers), Image Retrieval and Classification Techniques (9 papers), AI in cancer detection (8 papers) and Gastrointestinal Bleeding Diagnosis and Treatment (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (215 citations), Acoustics and Ultrasonics (8 citations), Signal Processing (89 citations), Computational Mechanics (144 citations) and Artificial Intelligence (193 citations). Sheng Li has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Xiongxiong He, Huang Bai, Jiajia Chen, Jian Ruan, Jinhui Zhu, Thomas F. George, Liping Chang, Yu Huang, Zhihui Zhu and Liangyu Chen. Their work appears in journals such as International Journal of Computer Assisted Radiology and Surgery, Neurocomputing, IEEE Transactions on Multimedia, IET Power Electronics and Flow Measurement and Instrumentation.

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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