Pingli Ma

432 total citations
9 papers, 250 citations indexed

About

Pingli Ma is a scholar working on Biophysics, Media Technology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Pingli Ma has authored 9 papers receiving a total of 250 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Biophysics, 6 papers in Media Technology and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Pingli Ma's work include Cell Image Analysis Techniques (7 papers), Image Processing Techniques and Applications (6 papers) and Digital Imaging for Blood Diseases (4 papers). Pingli Ma is often cited by papers focused on Cell Image Analysis Techniques (7 papers), Image Processing Techniques and Applications (6 papers) and Digital Imaging for Blood Diseases (4 papers). Pingli Ma collaborates with scholars based in China, Germany and United States. Pingli Ma's co-authors include Chen Li, Marcin Grzegorzek, Jiawei Zhang, Yudong Yao, Xin Zhao, Md Mamunur Rahaman, Tao Jiang, Jinghua Zhang, Hongzan Sun and Xinyu Huang and has published in prestigious journals such as Frontiers in Microbiology, Applied Sciences and Artificial Intelligence Review.

In The Last Decade

Pingli Ma

9 papers receiving 245 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Pingli Ma China	7	74	71	69	68	44	9	250
Reza Moradi Rad Canada	12	27 0.4×	109 1.5×	33 0.5×	228 3.4×	21 0.5×	23	388
Gloria M. Díaz Colombia	8	58 0.8×	104 1.5×	50 0.7×	214 3.1×	60 1.4×	36	332
Asok Kumar Maiti India	8	71 1.0×	165 2.3×	66 1.0×	206 3.0×	106 2.4×	10	350
Markus Hofmarcher Austria	4	53 0.7×	75 1.1×	23 0.3×	141 2.1×	14 0.3×	6	260
Yufan Luo China	10	19 0.3×	54 0.8×	23 0.3×	36 0.5×	31 0.7×	23	294
Salam Shuleenda Devi India	10	23 0.3×	53 0.7×	75 1.1×	187 2.8×	35 0.8×	21	247
Aimon Rahman Bangladesh	7	19 0.3×	97 1.4×	22 0.3×	118 1.7×	93 2.1×	8	225
Renjun Shuai China	8	30 0.4×	153 2.2×	21 0.3×	140 2.1×	86 2.0×	10	301
Afaf Tareef Australia	10	46 0.6×	214 3.0×	33 0.5×	247 3.6×	64 1.5×	21	350
Md. Aiub Hossain Bangladesh	4	58 0.8×	128 1.8×	59 0.9×	293 4.3×	112 2.5×	6	391

Countries citing papers authored by Pingli Ma

Since Specialization

Citations

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

Fields of papers citing papers by Pingli Ma

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pingli Ma

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

All Works

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9 of 9 papers shown

Li, Chen, Xin Zhao, Jiawei Zhang, et al.. (2023). EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluation. Frontiers in Microbiology. 14. 1084312–1084312. 6 indexed citations

Zhang, Jiawei, Chen Li, Md Mamunur Rahaman, et al.. (2022). A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements. Archives of Computational Methods in Engineering. 30(1). 639–673. 14 indexed citations

Li, Chen, Hongzan Sun, Peng Xu, et al.. (2022). TOD-CNN: An effective convolutional neural network for tiny object detection in sperm videos. Computers in Biology and Medicine. 146. 105543–105543. 27 indexed citations

Zhang, Jiawei, Pingli Ma, Tao Jiang, et al.. (2022). SEM-RCNN: A Squeeze-and-Excitation-Based Mask Region Convolutional Neural Network for Multi-Class Environmental Microorganism Detection. Applied Sciences. 12(19). 9902–9902. 17 indexed citations

Ma, Pingli, Chen Li, Md Mamunur Rahaman, et al.. (2022). A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches. Artificial Intelligence Review. 56(2). 1627–1698. 60 indexed citations

Li, Chen, Md Mamunur Rahaman, Hao Xu, et al.. (2022). EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification, and Detection Method Evaluation. Frontiers in Microbiology. 13. 829027–829027. 17 indexed citations

Zhang, Jiawei, Chen Li, Md Mamunur Rahaman, et al.. (2021). A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches. Artificial Intelligence Review. 55(4). 2875–2944. 101 indexed citations

Zhang, Jiawei, Chen Li, Md Mamunur Rahaman, et al.. (2021). A Comprehensive Review of Image Analysis Methods for Microorganism Counting: From Classical Image Processing to Deep Learning Approaches. arXiv (Cornell University). 7 indexed citations

Li, Chen, et al.. (2020). A Survey of Sperm Detection Techniques in Microscopic Videos. 219–224. 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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