Baofang Chang

457 citations
22 papers · 281 indexed · h-index 10
Topics
Radiomics and Machine Learning in Medical Imaging (5 papers)AI in cancer detection (5 papers)Medical Image Segmentation Techniques (3 papers)
Partner nations
ChinaSingaporeAustralia

In The Last Decade

Baofang Chang

20 papers receiving 276 citations

Peers

Baofang Chang
Comparison fields: 5 of 58
  • Computer Vision and Pattern Recognition 83
  • Plant Science 81
  • Artificial Intelligence 77
  • Radiology, Nuclear Medicine and Imaging 54
  • Analytical Chemistry 33
Replace Pedro H. Bugatti with:
Pedro H. Bugatti Brazil
Weijia Wu China
R. Cristin India
Gurram Sunitha India
Aboul Ella Hassenian Egypt
Qin Zhi-guang China
Ritesh Maurya India
Heba A. Elnemr Egypt
Neeraj Gupta India
Baofang Chang relative to Pedro H. Bugatti Brazil Pedro H. Bugatti's profile →
Citations per field
00.5×5.2×
Pedro H. Bugatti · 1×
Citations per year

Countries citing papers authored by Baofang Chang

Since Specialization
Citations

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

Fields of papers citing papers by Baofang Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Baofang Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Baofang Chang. A scholar is included among the top collaborators of Baofang Chang 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 Baofang Chang. Baofang Chang 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
#WorkIndexed citations
1 4
2 0
3 1
4 15
5 0
6 7
7 6
8 4
9 42
10 43
11 51
12 13
13 23
14 1
15 17
16 8
17 10
18 7
19 2
20 13

About Baofang Chang

Baofang Chang is a scholar working on Media Technology, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 22 papers that have together received 281 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (5 papers), AI in cancer detection (5 papers) and Medical Image Segmentation Techniques (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (83 citations), Media Technology (32 citations) and Analytical Chemistry (33 citations). Baofang Chang has collaborated with scholars based in China, Singapore and Australia. Frequent co-authors include Dong Liu, Guoqiang Li, Peiyan Yuan, Qing Zhao, Xiaoyan Zhao, Zhi Dou, Thambipillai Srikanthan, Jigang Wu, Xiaojian Ding and Wei Wang. Their work appears in journals such as Expert Systems with Applications, Frontiers in Plant Science and IEEE Transactions on Wireless Communications.

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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