Min-Chun Yang

565 citations
16 papers · 405 · h-index 10

Impact in

    • Advanced Image Fusion Techniques
    • Image Processing Techniques and Applications
    • Advanced Image Processing Techniques
    • Image and Signal Denoising Methods
    • Advanced Vision and Imaging
    • Image Enhancement Techniques

Papers in

Min-Chun Yang

15 papers receiving 389 citations

Peers

Min-Chun Yang
Comparison fields: 5 of 52
  • Media Technology 131
  • Computer Vision and Pattern Recognition 278
  • Radiology, Nuclear Medicine and Imaging 84
  • Artificial Intelligence 105
  • Computer Graphics and Computer-Aided Design 8
Replace Jiayin Kang with:
Jiayin Kang China
Rami Ben‐Ari Israel
Anas M. Ali Saudi Arabia
Xiuchao Sui Singapore
Mansur Vafadust Iran
Geet Sahu India
Alan Anwer Abdulla Iraq
Yimo Guo China
Chi-Hieu Pham France
Min-Chun Yang relative to Jiayin Kang China Jiayin Kang's profile →
Citations per field
00.5×1.6×
Jiayin Kang · 1×
Citations per year

Countries citing papers authored by Min-Chun Yang

Since Specialization
Citations

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

Fields of papers citing papers by Min-Chun Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Min-Chun Yang. 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 Min-Chun Yang. The network helps show where Min-Chun Yang may publish in the future.

Co-authors

The 25 scholars most cited alongside Min-Chun Yang, 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 Min-Chun Yang Line = papers co-authored together Min-Chun Yang links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 201291
2 201376
3 201273
4 201523
5 201523
6 201720
7 201217
8 201417
9 201817
10 201112
11 20119
12 20129
13 20158
14 20086
15 20124
16 20250

About Min-Chun Yang

Min-Chun Yang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Media Technology, having authored 16 papers that have together received 405 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (5 papers), AI in cancer detection (5 papers), Advanced Image Processing Techniques (5 papers), Ultrasound Imaging and Elastography (3 papers), Advanced Vision and Imaging (3 papers), MRI in cancer diagnosis (2 papers), Photoacoustic and Ultrasonic Imaging (2 papers) and Advanced Image Fusion Techniques (2 papers). The work is most often cited by research in Media Technology (131 citations), Computer Vision and Pattern Recognition (278 citations), Radiology, Nuclear Medicine and Imaging (84 citations), Artificial Intelligence (105 citations) and Computer Graphics and Computer-Aided Design (8 citations). Min-Chun Yang has collaborated with scholars based in Taiwan, United States and South Korea. Frequent co-authors include Yu-Chiang Frank Wang, Ruey‐Feng Chang, Chiun‐Sheng Huang, Woo Kyung Moon, De-An Huang, Li‐Wei Kang, Chia‐Wen Lin, Min Sun Bae, Jeon‐Hor Chen and Jeon‐Hor Chen. Their work appears in journals such as Ultrasound in Medicine & Biology, IEEE Transactions on Medical Imaging, IEEE Transactions on Image Processing, Ultrasonics and IEEE Transactions on Multimedia.

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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