Chaijie Duan

475 citations
27 papers · 336 indexed · h-index 10

Chaijie Duan

25 papers receiving 329 citations

Peers

Chaijie Duan
Comparison fields: 5 of 66
  • Computer Vision and Pattern Recognition 99
  • Radiology, Nuclear Medicine and Imaging 109
  • Urology 26
  • Biophysics 18
  • Analytical Chemistry 27
Replace Christian Daul with:
Christian Daul France
R.G. Aarnink Netherlands
Hirokazu Nosato Japan
M. Murat Dundar United States
Zheng Cao China
Yuting Guo China
Zhaobang Liu China
Qikui Zhu United States
Liyan Lin China
Adolf Lorenz Germany
Chaijie Duan relative to Christian Daul France Christian Daul's profile →
Citations per field
00.5×8.7×
Christian Daul · 1×
Citations per year

Countries citing papers authored by Chaijie Duan

Since Specialization
Citations

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

Fields of papers citing papers by Chaijie Duan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20242
2 20243
3 20239
4 201655
5 20164
6 201522
7 20151
8 20153
9 201323
10 20135
11 201310
12 201221
13 201112
14 20114
15 201055
16
Segmenting bladder wall and estimating wall thickness for magnetic resonance virtual cystoscopy
20101
17 20103
18 20101
19 201010
20 20055

About Chaijie Duan

Chaijie Duan is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Biophysics, having authored 27 papers that have together received 336 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (10 papers), Bladder and Urothelial Cancer Treatments (7 papers), Colorectal Cancer Screening and Detection (5 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), MRI in cancer diagnosis (3 papers), Medical Imaging Techniques and Applications (3 papers), AI in cancer detection (3 papers) and Pediatric Urology and Nephrology Studies (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (99 citations), Radiology, Nuclear Medicine and Imaging (109 citations) and Urology (26 citations). Chaijie Duan has collaborated with scholars based in China, United States and France. Frequent co-authors include Zhengrong Liang, Hao Han, Hongbin Zhu, Jing Wang, Guangxiang Zhang, Hongbing Lu, Yang Zhao, Honghui He, Fanghua Liu and Guoqing Lv. Their work appears in journals such as PLoS ONE, IEEE Antennas and Wireless Propagation Letters, IEEE Transactions on Biomedical Engineering, IEEE Transactions on Medical Imaging and Journal of Biophotonics.

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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