Yang Xiao

2.8k total citations · 1 hit paper
102 papers, 2.0k citations indexed

About

Yang Xiao is a scholar working on Biomedical Engineering, Radiology, Nuclear Medicine and Imaging and Mechanics of Materials. According to data from OpenAlex, Yang Xiao has authored 102 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Biomedical Engineering, 39 papers in Radiology, Nuclear Medicine and Imaging and 21 papers in Mechanics of Materials. Recurrent topics in Yang Xiao's work include Ultrasound Imaging and Elastography (32 papers), Ultrasonics and Acoustic Wave Propagation (19 papers) and Photoacoustic and Ultrasonic Imaging (18 papers). Yang Xiao is often cited by papers focused on Ultrasound Imaging and Elastography (32 papers), Ultrasonics and Acoustic Wave Propagation (19 papers) and Photoacoustic and Ultrasonic Imaging (18 papers). Yang Xiao collaborates with scholars based in China, Hong Kong and Taiwan. Yang Xiao's co-authors include Hairong Zheng, Congzhi Wang, Jinhua Yu, Yuanyuan Wang, Qi Zhang, Jun Shi, Lili Niu, Yanyan Yu, Long Meng and Yixin Hu and has published in prestigious journals such as Advanced Materials, Journal of Biological Chemistry and Nature Communications.

In The Last Decade

Yang Xiao

92 papers receiving 2.0k citations

Hit Papers

Deep learning radiomics can predict axillary lymph node s... 2020 2026 2022 2024 2020 100 200 300 400

Peers

Yang Xiao
Comparison fields: 5 of 144
  • Radiology, Nuclear Medicine and Imaging 1.0k
  • Biomedical Engineering 694
  • Artificial Intelligence 501
  • Molecular Biology 227
  • Pulmonary and Respiratory Medicine 196
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Citations per field, relative to Yang Xiao
Yang Xiao · 1×
Citations per year, relative to Yang Xiao
Yang Xiao · 1×

Countries citing papers authored by Yang Xiao

Since Specialization
Citations

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

Fields of papers citing papers by Yang Xiao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yang Xiao

This figure shows the co-authorship network connecting the top 25 collaborators of Yang Xiao. A scholar is included among the top collaborators of Yang Xiao 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 Yang Xiao. Yang Xiao 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
# Work Indexed citations
1 0
2 0
3 0
4 1
5 3
6 0
7 1
8 12
9 6
10 4
11 8
12 1
13 9
14 118
15 13
16
Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer breakdown →
402
17 39
18 180
19 22
20
Carrier Transport Characteristics of the Soluble PPV Polymer with Main Chain Containing Electron-Transporting Group
1

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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