Cheng Wan

42 papers receiving 293 citations

Peers

Cheng Wan
Comparison fields: 5 of 91
  • Computer Graphics and Computer-Aided Design 25
  • Health Informatics 5
  • Computer Vision and Pattern Recognition 71
  • Hardware and Architecture 21
  • Software 9
Replace Anselmo Montenegro with:
Anselmo Montenegro Brazil
Steven Senger United States
Ruihan Hu China
Sara Sabour United States
David Chelberg United States
Eduardo Juárez Spain
Hiroyuki Kubo Japan
M. Pauline Baker United States
Irena Galić Croatia
Enric Martı́ Spain
Cheng Wan relative to Anselmo Montenegro Brazil Anselmo Montenegro's profile →
Citations per field
00.5×10×15×19×
Anselmo Montenegro · 1×
Citations per year

Countries citing papers authored by Cheng Wan

Since Specialization
Citations

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

Fields of papers citing papers by Cheng Wan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 47 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202349
2 202340
3 202431
4 201516
5 202414
6 201614
7 202311
8 20249
9 20229
10 20108
11 20247
12 20237
13 20217
14 20226
15 20155
16 20234
17 20244
18 20224
19 20234
20 20224

About Cheng Wan

Cheng Wan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Information Systems and Signal Processing, having authored 47 papers that have together received 298 indexed citations. Recurring topics across this work include Service-Oriented Architecture and Web Services (4 papers), Advanced Vision and Imaging (4 papers), Computer Graphics and Visualization Techniques (4 papers), Data Management and Algorithms (3 papers), 3D Shape Modeling and Analysis (3 papers), Brain Tumor Detection and Classification (3 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Machine Learning in Healthcare (3 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (25 citations), Health Informatics (5 citations), Computer Vision and Pattern Recognition (71 citations), Hardware and Architecture (21 citations) and Software (9 citations). Cheng Wan has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Chaojian Li, Yingyan Lin, Sixu Li, Yonggan Fu, Yang Zhao, Haoran You, Yongan Zhang, Zhongzhi Yu, Hong Li and Zhiqi Li. Their work appears in journals such as Frontiers in Psychiatry, Academic Radiology, Knowledge-Based Systems, IEEE Micro and SIAM Journal on Optimization.

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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