Fujun Liu

1.5k total citations
21 papers, 870 citations indexed

About

Fujun Liu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Fujun Liu has authored 21 papers receiving a total of 870 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 5 papers in Molecular Biology. Recurrent topics in Fujun Liu's work include AI in cancer detection (7 papers), Muscle Physiology and Disorders (5 papers) and Cell Image Analysis Techniques (3 papers). Fujun Liu is often cited by papers focused on AI in cancer detection (7 papers), Muscle Physiology and Disorders (5 papers) and Cell Image Analysis Techniques (3 papers). Fujun Liu collaborates with scholars based in United States, China and Australia. Fujun Liu's co-authors include Lin Yang, Fuyong Xing, Hai Su, Yuanpu Xie, Xiaoshuang Shi, Manish Sapkota, Jinzheng Cai, Jonah D. Lee, Jyothi Mula and Charlotte A. Peterson and has published in prestigious journals such as Journal of Applied Physiology, American Journal Of Pathology and Pattern Recognition.

In The Last Decade

Fujun Liu

19 papers receiving 858 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fujun Liu United States 10 439 301 246 176 149 21 870
Philipp Kainz Austria 10 605 1.4× 404 1.3× 402 1.6× 97 0.6× 150 1.0× 17 1.1k
Yunbo Guo China 9 461 1.1× 394 1.3× 322 1.3× 115 0.7× 65 0.4× 28 872
André Homeyer Germany 13 376 0.9× 229 0.8× 294 1.2× 70 0.4× 117 0.8× 37 835
Xiaojun Guan China 4 601 1.4× 343 1.1× 346 1.4× 218 1.2× 171 1.1× 10 924
Dimitris Glotsos Greece 16 424 1.0× 219 0.7× 321 1.3× 107 0.6× 86 0.6× 73 868
Paul Salama United States 16 273 0.6× 468 1.6× 183 0.7× 261 1.5× 250 1.7× 115 1.1k
James Monaco United States 14 524 1.2× 345 1.1× 244 1.0× 138 0.8× 138 0.9× 37 761
N. K. Timofeeva Netherlands 4 717 1.6× 292 1.0× 471 1.9× 69 0.4× 143 1.0× 10 963
Heung‐Kook Choi South Korea 16 259 0.6× 334 1.1× 210 0.9× 77 0.4× 57 0.4× 89 814
Le Hou United States 14 790 1.8× 477 1.6× 489 2.0× 88 0.5× 163 1.1× 27 1.2k

Countries citing papers authored by Fujun Liu

Since Specialization
Citations

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

Fields of papers citing papers by Fujun Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fujun Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Fujun Liu. A scholar is included among the top collaborators of Fujun Liu 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 Fujun Liu. Fujun Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Zhang, Zizhao, Pingjun Chen, Mason McGough, et al.. (2019). Publisher Correction: Pathologist-level interpretable whole-slide cancer diagnosis with deep learning. Nature Machine Intelligence. 1(6). 289–289. 2 indexed citations
3.
Zhang, Zizhao, Pingjun Chen, Mason McGough, et al.. (2019). Pathologist-level interpretable whole-slide cancer diagnosis with deep learning. Nature Machine Intelligence. 1(5). 236–245. 189 indexed citations
4.
Zhang, Zizhao, Pingjun Chen, Mason McGough, et al.. (2019). Publisher Correction: Pathologist-level interpretable whole-slide cancer diagnosis with deep learning. Nature Machine Intelligence. 1(8). 384–384. 3 indexed citations
5.
Shi, Xiaoshuang, Fuyong Xing, Zhenhua Guo, et al.. (2018). Structured orthogonal matching pursuit for feature selection. Neurocomputing. 349. 164–172. 7 indexed citations
6.
Cai, Jinzheng, Fuyong Xing, Abhinandan Batra, et al.. (2018). Texture analysis for muscular dystrophy classification in MRI with improved class activation mapping. Pattern Recognition. 86. 368–375. 41 indexed citations
7.
Xing, Fuyong, Yuanpu Xie, Hai Su, Fujun Liu, & Lin Yang. (2017). Deep Learning in Microscopy Image Analysis: A Survey. IEEE Transactions on Neural Networks and Learning Systems. 29(10). 4550–4568. 291 indexed citations
8.
Sapkota, Manish, Fujun Liu, Yuanpu Xie, et al.. (2017). AIIMDs: An Integrated Framework of Automatic Idiopathic Inflammatory Myopathy Diagnosis for Muscle. IEEE Journal of Biomedical and Health Informatics. 22(3). 942–954. 2 indexed citations
9.
Meng, Hui, Lin Yang, Fujun Liu, et al.. (2016). Treatment with ActRIIB-mFc Produces Myofiber Growth and Improves Lifespan in the Acta1 H40Y Murine Model of Nemaline Myopathy. American Journal Of Pathology. 186(6). 1568–1581. 21 indexed citations
10.
Gao, Hongyun, et al.. (2016). A New Model Based on GEP-SWPM for Predicting Heavy Metals Speciation. 2122–2126. 1 indexed citations
11.
Lee, Jonah D., Christopher S. Fry, Jyothi Mula, et al.. (2015). Aged Muscle Demonstrates Fiber-Type Adaptations in Response to Mechanical Overload, in the Absence of Myofiber Hypertrophy, Independent of Satellite Cell Abundance. The Journals of Gerontology Series A. 71(4). 461–467. 42 indexed citations
12.
Xie, Yuanpu, Xiangfei Kong, Fuyong Xing, et al.. (2015). Deep Voting: A Robust Approach Toward Nucleus Localization in Microscopy Images. Lecture notes in computer science. 9351. 374–382. 49 indexed citations
13.
14.
Lawlor, Michael W., Marissa G. Viola, Hui Meng, et al.. (2014). Differential Muscle Hypertrophy Is Associated with Satellite Cell Numbers and Akt Pathway Activation Following Activin Type IIB Receptor Inhibition in Mtm1 p.R69C Mice. American Journal Of Pathology. 184(6). 1831–1842. 26 indexed citations
15.
Liu, Fujun, Fuyong Xing, Hai Su, & Lin Yang. (2014). Touching adipocyte cells decomposition using combinatorial optimization. 30. 1340–1347.
16.
Liu, Fujun, Fuyong Xing, & Lin Yang. (2014). Robust muscle cell segmentation using region selection with dynamic programming. 54. 521–524. 4 indexed citations
17.
Li, Qing, et al.. (2012). Improved VSM Algorithm and Its Application in FAQ. Jisuanji gongcheng.
18.
Mula, Jyothi, Jonah D. Lee, Fujun Liu, Lin Yang, & Charlotte A. Peterson. (2012). Automated image analysis of skeletal muscle fiber cross-sectional area. Journal of Applied Physiology. 114(1). 148–155. 51 indexed citations
19.
Ge, Tingjian & Fujun Liu. (2012). Accuracy-Aware Uncertain Stream Databases. 174–185. 1 indexed citations
20.
Liu, Fujun, et al.. (2011). Emergency Rescue Decision Assistant System Based on GIS for Natural Gas Pipeline. 22. 304–314. 2 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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