Fei Ma

1.4k total citations · 1 hit paper
90 papers, 820 citations indexed

About

Fei Ma is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Fei Ma has authored 90 papers receiving a total of 820 indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Computer Vision and Pattern Recognition, 32 papers in Artificial Intelligence and 12 papers in Signal Processing. Recurrent topics in Fei Ma's work include AI in cancer detection (15 papers), Image Retrieval and Classification Techniques (9 papers) and Medical Image Segmentation Techniques (9 papers). Fei Ma is often cited by papers focused on AI in cancer detection (15 papers), Image Retrieval and Classification Techniques (9 papers) and Medical Image Segmentation Techniques (9 papers). Fei Ma collaborates with scholars based in China, United Kingdom and Australia. Fei Ma's co-authors include Limin Yu, Eng Gee Lim, Miguel López‐Benítez, Tiantian Guo, Tongpo Zhang, Mariusz Bajger, Murk J. Bottema, Jionglong Su, John Slavotinek and Weifen Zhuang and has published in prestigious journals such as Chemical Engineering Journal, Small and Electrochimica Acta.

In The Last Decade

Fei Ma

81 papers receiving 785 citations

Hit Papers

A Review of Wavelet Analysis and Its Applications: Challe... 2022 2026 2023 2024 2022 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities
    2022 255 citations Tiantian Guo, Tongpo Zhang et al. IEEE Access profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fei Ma China 12 240 216 122 97 88 90 820
Xiaohan Ding China 10 187 0.8× 472 2.2× 64 0.5× 26 0.3× 64 0.7× 40 1.0k
Ian Nabney United Kingdom 7 285 1.2× 162 0.8× 73 0.6× 88 0.9× 51 0.6× 20 860
Yaping Huang China 19 266 1.1× 607 2.8× 70 0.6× 54 0.6× 46 0.5× 92 1.2k
Francesco Isgrò Italy 14 239 1.0× 357 1.7× 77 0.6× 38 0.4× 37 0.4× 66 903
Tao Zhou China 14 365 1.5× 274 1.3× 67 0.5× 45 0.5× 105 1.2× 132 1.1k
Kari Saarinen Finland 9 156 0.7× 659 3.1× 99 0.8× 88 0.9× 113 1.3× 38 1.3k
Ying Qu China 20 315 1.3× 589 2.7× 80 0.7× 44 0.5× 193 2.2× 104 2.1k
Ke Lv China 14 103 0.4× 169 0.8× 57 0.5× 53 0.5× 28 0.3× 86 575

Countries citing papers authored by Fei Ma

Since Specialization
Citations

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

Fields of papers citing papers by Fei Ma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fei Ma

This figure shows the co-authorship network connecting the top 25 collaborators of Fei Ma. A scholar is included among the top collaborators of Fei Ma 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 Fei Ma. Fei Ma 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.
Ma, Fei, Ting Meng, Junwei Li, et al.. (2025). Photo-motivated heterojunctions coupling built-in electric field stimulating sulfur redox kinetics for lithium-sulfur batteries. Chemical Engineering Journal. 506. 160356–160356. 4 indexed citations
2.
Chen, Xuhang, et al.. (2025). Tag-Enriched Multi-Attention With Large Language Models for Cross-Domain Sequential Recommendation. IEEE Transactions on Consumer Electronics. 72(1). 1130–1137.
3.
Yu, Limin, et al.. (2025). Retrosynthesis Prediction via Search in (Hyper) Graph. IEEE Transactions on Automation Science and Engineering. 22. 17749–17760.
4.
Dai, Tianhong, et al.. (2025). Generative Prompt Controlled Diffusion for weakly supervised semantic segmentation. Neurocomputing. 638. 130103–130103. 7 indexed citations
5.
Yu, Limin, et al.. (2025). Semi-template framework for retrosynthesis prediction using graph neural network. Pattern Recognition. 168. 111825–111825. 1 indexed citations
6.
Qiu, Xiaoyu, et al.. (2025). Image augmentation agent for weakly supervised semantic segmentation. Neurocomputing. 654. 131314–131314. 2 indexed citations
7.
Ma, Fei, et al.. (2024). More Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference. IEEE Transactions on Knowledge and Data Engineering. 36(11). 6588–6599. 6 indexed citations
8.
Ma, Fei, et al.. (2024). RCsearcher: Reaction center identification in retrosynthesis via deep Q-learning. Pattern Recognition. 150. 110318–110318. 5 indexed citations
9.
Yu, Limin, et al.. (2024). Fully Automatic Karyotyping via Deep Convolutional Neural Networks. IEEE Access. 12. 46081–46092. 2 indexed citations
10.
11.
Yu, Limin, et al.. (2023). Down Syndrome detection with Swin Transformer architecture. Biomedical Signal Processing and Control. 86. 105199–105199. 7 indexed citations
12.
Choubineh, Abouzar, et al.. (2023). Applying Monte Carlo Dropout to Quantify the Uncertainty of Skip Connection-Based Convolutional Neural Networks Optimized by Big Data. Electronics. 12(6). 1453–1453. 8 indexed citations
13.
Choubineh, Abouzar, et al.. (2023). Deep ensemble learning for high-dimensional subsurface fluid flow modeling. Engineering Applications of Artificial Intelligence. 126. 106968–106968. 1 indexed citations
14.
Guo, Tiantian, Tongpo Zhang, Eng Gee Lim, et al.. (2022). A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities. IEEE Access. 10. 58869–58903. 255 indexed citations breakdown →
15.
Choubineh, Abouzar, et al.. (2022). An innovative application of deep learning in multiscale modeling of subsurface fluid flow: Reconstructing the basis functions of the mixed GMsFEM. Journal of Petroleum Science and Engineering. 216. 110751–110751. 3 indexed citations
16.
Yu, Limin, et al.. (2022). AEDNet: Adaptive Edge-Deleting Network For Subgraph Matching. Pattern Recognition. 133. 109033–109033. 11 indexed citations
17.
Ma, Fei, Lei Yang, Hui‐Yuan Wang, et al.. (2022). The effect of trait‐based diversity on productivity results mainly from intraspecific trait variability in the macrophyte community. Freshwater Biology. 67(7). 1137–1149. 9 indexed citations
18.
Ma, Fei, et al.. (2020). Length-of-Stay Prediction for Pediatric Patients With Respiratory Diseases Using Decision Tree Methods. IEEE Journal of Biomedical and Health Informatics. 24(9). 2651–2662. 24 indexed citations
19.
Ma, Fei, Limin Yu, Mariusz Bajger, & Murk J. Bottema. (2015). Incorporation of fuzzy spatial relation in temporal mammogram registration. Fuzzy Sets and Systems. 279. 87–100. 6 indexed citations
20.
Ma, Fei. (2007). SVM-based Incremental Learning Method and its Application. Aeronautical Computing Technique. 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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