Lichao Wang

1.3k total citations · 1 hit paper
9 papers, 696 citations indexed

About

Lichao Wang is a scholar working on Computer Vision and Pattern Recognition, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, Lichao Wang has authored 9 papers receiving a total of 696 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Computer Vision and Pattern Recognition, 2 papers in Molecular Biology and 2 papers in Artificial Intelligence. Recurrent topics in Lichao Wang's work include Advanced Image Processing Techniques (2 papers), Cell Image Analysis Techniques (2 papers) and Atmospheric and Environmental Gas Dynamics (1 paper). Lichao Wang is often cited by papers focused on Advanced Image Processing Techniques (2 papers), Cell Image Analysis Techniques (2 papers) and Atmospheric and Environmental Gas Dynamics (1 paper). Lichao Wang collaborates with scholars based in Germany, China and United States. Lichao Wang's co-authors include Nassir Navab, Tingying Peng, Maximilian Baust, Shadi Albarqouni, Amit Sethi, Katja Steiger, Anna Melissa Schlitter, Iréne Esposito, Abhishek Vahadane and Timm Schroeder and has published in prestigious journals such as Nature Communications, IEEE Transactions on Medical Imaging and Journal of Energy Storage.

In The Last Decade

Lichao Wang

7 papers receiving 681 citations

Hit Papers

Structure-Preserving Color Normalization and Sparse Stain... 2016 2026 2019 2022 2016 100 200 300 400

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Lichao Wang Germany 6 395 245 236 179 117 9 696
Fujun Liu United States 10 439 1.1× 301 1.2× 246 1.0× 149 0.8× 176 1.5× 21 870
Navid Farahani United States 11 461 1.2× 163 0.7× 261 1.1× 173 1.0× 115 1.0× 17 823
Marcial García‐Rojo Spain 15 349 0.9× 155 0.6× 181 0.8× 153 0.9× 109 0.9× 57 682
Philipp Kainz Austria 10 605 1.5× 404 1.6× 402 1.7× 150 0.8× 97 0.8× 17 1.1k
Bassem Ben Cheikh France 6 443 1.1× 344 1.4× 299 1.3× 80 0.4× 136 1.2× 18 778
Xiaojun Guan China 4 601 1.5× 343 1.4× 346 1.5× 171 1.0× 218 1.9× 10 924
Monjoy Saha India 10 371 0.9× 172 0.7× 269 1.1× 91 0.5× 57 0.5× 18 559
Ezgi Mercan United States 17 449 1.1× 215 0.9× 306 1.3× 97 0.5× 69 0.6× 49 913
Guillaume Jaume United States 11 577 1.5× 210 0.9× 401 1.7× 107 0.6× 124 1.1× 14 885
Maschenka Balkenhol Netherlands 13 685 1.7× 298 1.2× 530 2.2× 139 0.8× 70 0.6× 21 906

Countries citing papers authored by Lichao Wang

Since Specialization
Citations

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

Fields of papers citing papers by Lichao Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lichao Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Lichao Wang. A scholar is included among the top collaborators of Lichao Wang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Lichao Wang. Lichao Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

9 of 9 papers shown
1.
Wang, Lichao, et al.. (2025). A CNN-LSTM hybrid network with transfer learning for accurate lithium-ion battery state of health estimation. Journal of Energy Storage. 141. 119450–119450.
3.
Meng, Ziyao, et al.. (2022). Defect object detection algorithm for electroluminescence image defects of photovoltaic modules based on deep learning. Energy Science & Engineering. 10(3). 800–813. 33 indexed citations
4.
Peng, Tingying, Kurt S. Thorn, Timm Schroeder, et al.. (2017). A BaSiC tool for background and shading correction of optical microscopy images. Nature Communications. 8(1). 14836–14836. 188 indexed citations
5.
Albarqouni, Shadi, et al.. (2016). Single-view X-ray depth recovery: toward a novel concept for image-guided interventions. International Journal of Computer Assisted Radiology and Surgery. 11(6). 873–880. 5 indexed citations
6.
Vahadane, Abhishek, Tingying Peng, Amit Sethi, et al.. (2016). Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images. IEEE Transactions on Medical Imaging. 35(8). 1962–1971. 453 indexed citations breakdown →
7.
Peng, Tingying, Lichao Wang, Christine Bayer, et al.. (2014). Shading Correction for Whole Slide Image Using Low Rank and Sparse Decomposition. Lecture notes in computer science. 17(Pt 1). 33–40. 9 indexed citations
8.
Yang, Mei, et al.. (2011). Review on Applications of DMSP/OLS Night-time Emissions Data. Yaogan jishu yu yingyong. 26(1). 45–51. 7 indexed citations
9.
Wang, Lichao, et al.. (2010). Evaluation and Analysis on Image Fusion of ETM. Yaogan jishu yu yingyong. 22(6). 733–738. 1 indexed citations

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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