Chenxin Li

88 papers receiving 1.6k citations

Chenxin Li's Hit Papers

U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation 2025 · 105 citations
1050+1+2Years since publication4080120

Peers

Chenxin Li
Comparison fields: 5 of 156
  • Computer Vision and Pattern Recognition 282
  • Biological Psychiatry 23
  • Artificial Intelligence 224
  • Radiology, Nuclear Medicine and Imaging 132
  • Plant Science 227
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Countries citing papers authored by Chenxin Li

Since Specialization
Citations

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

Fields of papers citing papers by Chenxin Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 98 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Single-cell multi-omics in the medicinal plant Catharanthus roseus
Hit paper breakdown →
2023126
2 2019112
3
U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation
Hit paper breakdown →
2025105
4 2021103
5 201782
6 202167
7 201750
8 201046
9 202437
10 202132
11 200631
12 202430
13 200730
14 202328
15 202025
16 202125
17 202324
18 202224
19 202423
20 202123

About Chenxin Li

Chenxin Li is a scholar working on Molecular Biology, Artificial Intelligence, Computer Vision and Pattern Recognition, Plant Science and Radiology, Nuclear Medicine and Imaging, having authored 98 papers that have together received 1.6k indexed citations. Recurring topics across this work include AI in cancer detection (7 papers), Genomics and Phylogenetic Studies (5 papers), Plant Molecular Biology Research (4 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), Medical Image Segmentation Techniques (3 papers), Advanced Antenna and Metasurface Technologies (3 papers), Advanced Neural Network Applications (3 papers) and Domain Adaptation and Few-Shot Learning (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (282 citations), Biological Psychiatry (23 citations), Artificial Intelligence (224 citations), Radiology, Nuclear Medicine and Imaging (132 citations) and Plant Science (227 citations). Chenxin Li has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Xinghao Ding, Yue Huang, C. Robin Buell, Yizhou Yu, Yixuan Yuan, Hengyu Liu, Wuyang Li, Liyan Sun, Venkatesan Sundaresan and John P. Hamilton. Their work appears in journals such as Computers in Biology and Medicine, Nucleic Acids Research, New Phytologist, Journal of Applied Probability and Diabetes Research and Clinical Practice.

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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