Jun Shu

492 citations
24 papers · 373 · h-index 10

Impact in

Papers in

Jun Shu

21 papers receiving 365 citations

Peers

Jun Shu
Comparison fields: 5 of 66
  • Pulmonary and Respiratory Medicine 155
  • Radiology, Nuclear Medicine and Imaging 82
  • Health Informatics 3
  • Rheumatology 21
  • Oncology 27
Replace John Shyi Peng Yuen with:
John Shyi Peng Yuen Singapore
Yoshihito Souma Japan
Antonio Notaristefano Italy
Dianjun Wang China
Vladimir Mozetič Croatia
Yi‐Chen Lai Taiwan
Koichiro Kimura Japan
Ali Barah Qatar
Jun Shu relative to John Shyi Peng Yuen Singapore John Shyi Peng Yuen's profile →
Citations per field
00.5×3.1×
John Shyi Peng Yuen · 1×
Citations per year

Countries citing papers authored by Jun Shu

Since Specialization
Citations

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

Fields of papers citing papers by Jun Shu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018110
2 201967
3 202328
4 202122
5 202021
6 201821
7 202016
8 201414
9 201213
10 202013
11 20178
12 20107
13
Precise and comprehensive evaluation of perianal fistulas, classification and related complications using magnetic resonance imaging.
20236
14 20136
15 20235
16 20165
17 20225
18 20222
19 20242
20 20251

About Jun Shu

Jun Shu is a scholar working on Pulmonary and Respiratory Medicine, Oncology, Radiology, Nuclear Medicine and Imaging, Molecular Biology and Surgery, having authored 24 papers that have together received 373 indexed citations. Recurring topics across this work include Renal cell carcinoma treatment (3 papers), 3D Shape Modeling and Analysis (3 papers), 3D Surveying and Cultural Heritage (3 papers), Lung Cancer Diagnosis and Treatment (2 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Advanced MRI Techniques and Applications (2 papers), Folate and B Vitamins Research (1 paper) and Robotics and Sensor-Based Localization (1 paper). The work is most often cited by research in Pulmonary and Respiratory Medicine (155 citations), Radiology, Nuclear Medicine and Imaging (82 citations), Health Informatics (3 citations), Rheumatology (21 citations) and Oncology (27 citations). Jun Shu has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Xiaoli Meng, Didi Wen, Hong Yin, Zhengting Cai, Yuwei Xia, Jingsong Zhang, Jingjing Cui, Shiqi Yu, Jie Zhang and Yibin Xi. Their work appears in journals such as European Journal of Radiology, Gene, BioMed Research International, Molecular Imaging and Biology and Orthopedics.

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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