Pan Su

1.1k citations
29 papers · 620 · 1 hit paper · h-index 13

Impact in

Papers in

Pan Su

29 papers receiving 609 citations

Pan Su's Hit Papers

CS 2 -Net: Deep learning segmentation of curvilinear structures in medical imaging 2020 · 240 citations
2400+2+4Years since publication50100150200

Peers

Pan Su
Comparison fields: 5 of 95
  • Ophthalmology 145
  • Radiology, Nuclear Medicine and Imaging 287
  • Computer Vision and Pattern Recognition 171
  • Health Informatics 11
  • Health Information Management 28
Replace Ivan Cruz‐Aceves with:
Ivan Cruz‐Aceves Mexico
Khan Bahadar Khan Pakistan
Qi Yu China
Qingyao Wu China
Ali Mohammad Alqudah Jordan
Xiaosheng Yu China
Nina Zhou United States
Jitae Shin South Korea
Minglei Li China
Meenakshi Sood India
Pan Su relative to Ivan Cruz‐Aceves Mexico Ivan Cruz‐Aceves's profile →
Citations per field
00.5×1.5×1.9×
Ivan Cruz‐Aceves · 1×
Citations per year

Countries citing papers authored by Pan Su

Since Specialization
Citations

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

Fields of papers citing papers by Pan Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1
CS 2 -Net: Deep learning segmentation of curvilinear structures in medical imaging
Hit paper breakdown →
2020240
2 201848
3 202044
4 201941
5 202030
6 201524
7 201624
8 202121
9 202220
10 202020
11 202118
12 202217
13 201915
14 202210
15 201910
16 20198
17 20184
18 20144
19 20183
20 20232

About Pan Su

Pan Su is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Ophthalmology, Public Health, Environmental and Occupational Health and Biomedical Engineering, having authored 29 papers that have together received 620 indexed citations. Recurring topics across this work include Fuzzy Logic and Control Systems (7 papers), Retinal Imaging and Analysis (6 papers), Ocular Surface and Contact Lens (5 papers), Corneal surgery and disorders (5 papers), Glaucoma and retinal disorders (4 papers), Rough Sets and Fuzzy Logic (4 papers), Multi-Criteria Decision Making (3 papers) and Retinal Diseases and Treatments (3 papers). The work is most often cited by research in Ophthalmology (145 citations), Radiology, Nuclear Medicine and Imaging (287 citations), Computer Vision and Pattern Recognition (171 citations), Health Informatics (11 citations) and Health Information Management (28 citations). Pan Su has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Tianhua Chen, Yitian Zhao, Qiang Shen, Changjing Shang, Jiang Liu, Yalin Zheng, Yonghuai Liu, Jun Cheng, Lei Mou and Masahiro Akïba. Their work appears in journals such as IEEE Transactions on Medical Imaging, IEEE Access, Artificial Intelligence in Medicine, International Journal of Machine Learning and Cybernetics and Medical Physics.

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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