Zhen Ma

58 papers receiving 1.2k citations

Peers

Zhen Ma
Comparison fields: 5 of 132
  • Computer Vision and Pattern Recognition 450
  • Oncology 339
  • Biophysics 68
  • Artificial Intelligence 359
  • Media Technology 86
Replace Alfredo Paolillo with:
Alfredo Paolillo Italy
Evgin Göçeri Türkiye
Nader Karimi Iran
Şaban Öztürk Türkiye
Joanna Jaworek-Korjakowska Poland
Ashnil Kumar Australia
Kumar Abhishek India
Majed Alhaisoni Saudi Arabia
Muhammad Almas Anjum Pakistan
Mohammed A. Al‐masni South Korea
Zhen Ma relative to Alfredo Paolillo Italy Alfredo Paolillo's profile →
Citations per field
00.5×3.4×
Alfredo Paolillo · 1×
Citations per year

Countries citing papers authored by Zhen Ma

Since Specialization
Citations

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

Fields of papers citing papers by Zhen Ma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Zhen Ma, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Zhen Ma Line = papers co-authored together Zhen Ma links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 64 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2009235
2 2016182
3 2015154
4 200992
5 201567
6 201551
7 201645
8 201744
9 201543
10 201142
11 201037
12 201636
13 200424
14 201523
15 201215
16 201315
17 202411
18 201511
19 202310
20 201710

About Zhen Ma

Zhen Ma is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Networks and Communications, Biomedical Engineering and Oncology, having authored 64 papers that have together received 1.3k indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (10 papers), AI in cancer detection (8 papers), Cutaneous Melanoma Detection and Management (5 papers), Medical Imaging and Analysis (5 papers), Image and Object Detection Techniques (4 papers), Pelvic floor disorders treatments (3 papers), 3D Shape Modeling and Analysis (3 papers) and MRI in cancer diagnosis (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (450 citations), Oncology (339 citations), Biophysics (68 citations), Artificial Intelligence (359 citations) and Media Technology (86 citations). Zhen Ma has collaborated with scholars based in China, Portugal and Brazil. Frequent co-authors include João Manuel R. S. Tavares, Renato Natal Jorge, M. Filho, Aledir Silveira Pereira, João Paulo Papa, Roberta B. Oliveira, Degan Zhang, Yuexian Hou, Feifei Gao and Gongpu Wang. Their work appears in journals such as Sensors, International Journal of Biological Macromolecules, Computer Methods and Programs in Biomedicine, Engineering Computations and Engineering Applications of Artificial Intelligence.

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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