Baiying Lei

12.4k citations
296 papers · 8.1k indexed · 4 hit papers · h-index 48
Topics
AI in cancer detection (63 papers)Radiomics and Machine Learning in Medical Imaging (34 papers)Functional Brain Connectivity Studies (31 papers)

In The Last Decade

Baiying Lei

274 papers receiving 7.9k citations

Hit Papers

Deep Learning in Medical Ultrasound Analysis: A Review20192026202120232019202120212024100200300400500

Peers

Baiying Lei
Comparison fields: 5 of 179
  • Artificial Intelligence 3.1k
  • Computer Vision and Pattern Recognition 2.6k
  • Radiology, Nuclear Medicine and Imaging 2.4k
  • Neurology 1.0k
  • Cognitive Neuroscience 844
Replace Yong Xia with:
Yong Xia China
M. Jorge Cardoso United Kingdom
Guorong Wu United States
Heung‐Il Suk South Korea
Le Lü United States
Muhammad Attique Khan Pakistan
Dagan Feng Australia
Mohsen Ghafoorian Netherlands
Holger R. Roth United States
Weidong Cai Australia
Baiying Lei relative to Yong Xia China Yong Xia's profile →
Citations per field
00.5×2.7×
Yong Xia · 1×
Citations per year

Countries citing papers authored by Baiying Lei

Since Specialization
Citations

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

Fields of papers citing papers by Baiying Lei

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Baiying Lei

This figure shows the co-authorship network connecting the top 25 collaborators of Baiying Lei. A scholar is included among the top collaborators of Baiying Lei 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 Baiying Lei. Baiying Lei 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
#WorkIndexed citations
1 1
2 3
3 19
4 40
5 0
6 23
7 10
8 1
9 26
10 8
11 87
12 13
13 9
14 22
15 83
16 61
17 193
18 14
19 124
20 72

About Baiying Lei

Baiying Lei is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence, having authored 296 papers that have together received 8.1k indexed citations. Recurring topics across this work include AI in cancer detection (63 papers), Radiomics and Machine Learning in Medical Imaging (34 papers) and Functional Brain Connectivity Studies (31 papers). The work is most often cited by research in Health Informatics (191 citations), Computer Vision and Pattern Recognition (2.6k citations) and Neurology (1.0k citations). Baiying Lei has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Tianfu Wang, Dong Ni, Siping Chen, Feng Zhou, Jing Qin, Ahmed Elazab, Shuqiang Wang, Haijun Lei, Huisi Wu and Ing Yann Soon. Their work appears in journals such as PLoS ONE, IEEE Transactions on Pattern Analysis and Machine Intelligence and Scientific Reports.

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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