Feng Su

552 total citations
28 papers, 340 citations indexed

About

Feng Su is a scholar working on Cellular and Molecular Neuroscience, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, Feng Su has authored 28 papers receiving a total of 340 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Cellular and Molecular Neuroscience, 4 papers in Molecular Biology and 4 papers in Artificial Intelligence. Recurrent topics in Feng Su's work include Radiomics and Machine Learning in Medical Imaging (4 papers), Cell Image Analysis Techniques (3 papers) and Neural dynamics and brain function (3 papers). Feng Su is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (4 papers), Cell Image Analysis Techniques (3 papers) and Neural dynamics and brain function (3 papers). Feng Su collaborates with scholars based in China, United States and United Kingdom. Feng Su's co-authors include Zu‐Guo Yu, Fu-Yao Ren, Yajie Hu, Jiafu Ji, Peijiang Yuan, Jianmin Li, Xinya Zhao, Yu Sun, Qianyi Xiao and Qianhua Zhao and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and Bioresource Technology.

In The Last Decade

Feng Su

26 papers receiving 331 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Feng Su China 10 87 80 70 47 40 28 340
Thomas Chong United States 6 63 0.7× 96 1.2× 150 2.1× 71 1.5× 22 0.6× 9 570
O. Lange United States 11 97 1.1× 71 0.9× 52 0.7× 27 0.6× 33 0.8× 27 345
Zhaohui Zhu China 9 234 2.7× 30 0.4× 34 0.5× 56 1.2× 49 1.2× 25 442
Daniel Jiménez‐Carretero Spain 13 155 1.8× 105 1.3× 54 0.8× 18 0.4× 100 2.5× 36 575
Peng Xie China 15 36 0.4× 523 6.5× 47 0.7× 21 0.4× 22 0.6× 47 749
Thomas A. Geddes Australia 7 33 0.4× 242 3.0× 83 1.2× 22 0.5× 11 0.3× 10 467
Xiangtian Yu China 15 25 0.3× 541 6.8× 34 0.5× 35 0.7× 31 0.8× 48 740
Masao Ueki Japan 13 55 0.6× 189 2.4× 92 1.3× 23 0.5× 38 0.9× 45 569
Shan Cong China 11 35 0.4× 63 0.8× 113 1.6× 38 0.8× 21 0.5× 65 353
Weichen Song China 12 16 0.2× 257 3.2× 37 0.5× 22 0.5× 39 1.0× 39 483

Countries citing papers authored by Feng Su

Since Specialization
Citations

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

Fields of papers citing papers by Feng Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Feng Su

This figure shows the co-authorship network connecting the top 25 collaborators of Feng Su. A scholar is included among the top collaborators of Feng Su 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 Feng Su. Feng Su 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.
Su, Feng, Mengna Liu, K. Y. Shan, et al.. (2025). High‐throughput markerless pose estimation and home‐cage activity analysis of tree shrew using deep learning. Animal Models and Experimental Medicine. 8(5). 896–905.
2.
Su, Feng, et al.. (2023). Annotation-free glioma grading from pathological images using ensemble deep learning. Heliyon. 9(3). e14654–e14654. 12 indexed citations
3.
4.
5.
Wang, Yongjie, Feng Su, Lu Qian, et al.. (2023). Deep learning-driven diagnosis of multi-type vertebra diseases based on computed tomography images. Quantitative Imaging in Medicine and Surgery. 14(1). 800–813. 3 indexed citations
6.
Zhang, Weiyu, et al.. (2023). An automatic diagnostic system for the urodynamic study applying in lower urinary tract dysfunction. International Urology and Nephrology. 56(2). 441–449. 4 indexed citations
7.
Wei, Mengping, Lei Yang, Feng Su, et al.. (2023). ABHD6 drives endocytosis of AMPA receptors to regulate synaptic plasticity and learning flexibility. Progress in Neurobiology. 233. 102559–102559. 2 indexed citations
8.
Su, Feng, Yong Fang, Tian Jiang, et al.. (2022). The Single Nucleotide Polymorphisms of AP1S1 are Associated with Risk of Esophageal Squamous Cell Carcinoma in Chinese Population. SHILAP Revista de lepidopterología. 4 indexed citations
9.
Su, Feng, Mengping Wei, Meng Sun, et al.. (2022). Deep learning-based synapse counting and synaptic ultrastructure analysis of electron microscopy images. Journal of Neuroscience Methods. 384. 109750–109750. 3 indexed citations
10.
Su, Feng, Jianmin Li, Xinya Zhao, et al.. (2022). Interpretable tumor differentiation grade and microsatellite instability recognition in gastric cancer using deep learning. Laboratory Investigation. 102(6). 641–649. 24 indexed citations
11.
Su, Feng & Yonglu Tian. (2022). Non-rigid Registration for Two-photon Imaging Using Triangulation and Piecewise Affine Transformation. Neuroscience. 491. 1–12. 1 indexed citations
12.
Su, Feng, Mengping Wei, Chong Wang, et al.. (2022). Noninvasive Tracking of Every Individual in Unmarked Mouse Groups Using Multi-Camera Fusion and Deep Learning. Neuroscience Bulletin. 39(6). 893–910. 8 indexed citations
13.
Ding, Ding, Feng Su, Wanqing Wu, et al.. (2021). Disturbed microbial ecology in Alzheimer’s disease: evidence from the gut microbiota and fecal metabolome. BMC Microbiology. 21(1). 226–226. 66 indexed citations
14.
Hu, Yajie, Feng Su, Xinyu Wang, et al.. (2021). Deep learning system for lymph node quantification and metastatic cancer identification from whole-slide pathology images. Gastric Cancer. 24(4). 868–877. 40 indexed citations
15.
Zhang, Guodong, Feng Su, Yan Jiao, Qinghua Chen, & Duu‐Jong Lee. (2020). Biocathodic performance of bioelectrochemical systems operated at low temperature. Bioresource Technology. 310. 123463–123463. 7 indexed citations
16.
Wei, Mengping, Meng Wang, Jue Wang, et al.. (2020). PORCN Negatively Regulates AMPAR Function Independently of Subunit Composition and the Amino-Terminal and Carboxy-Terminal Domains of AMPARs. Frontiers in Cell and Developmental Biology. 8. 829–829. 5 indexed citations
17.
Jiang, Weibin, et al.. (2020). Wearable on-device deep learning system for hand gesture recognition based on FPGA accelerator. Mathematical Biosciences & Engineering. 18(1). 132–153. 18 indexed citations
18.
Su, Feng, Yajie Hu, Peijiang Yuan, et al.. (2020). Development and validation of a deep learning system for ascites cytopathology interpretation. Gastric Cancer. 23(6). 1041–1050. 36 indexed citations
19.
Su, Feng, et al.. (2016). The superior fault tolerance of artificial neural network training with a fault/noise injection-based genetic algorithm. Protein & Cell. 7(10). 735–748. 14 indexed citations
20.
Yuan, Peijiang, et al.. (2015). Autonomous path planning solution for industrial robot manipulator using backpropagation algorithm. Advances in Mechanical Engineering. 7(12). 3 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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