Yucheng Tang

7.7k citations
85 papers · 3.0k indexed · 2 hit papers · h-index 18
Topics
Radiomics and Machine Learning in Medical Imaging (21 papers)COVID-19 diagnosis using AI (14 papers)AI in cancer detection (13 papers)

In The Last Decade

Yucheng Tang

80 papers receiving 3.0k citations

Hit Papers

UNETR: Transformers for 3D Medical Image Segmentation2022202620232024202220224008001.2k

Peers

Yucheng Tang
Comparison fields: 5 of 136
  • Computer Vision and Pattern Recognition 1.3k
  • Radiology, Nuclear Medicine and Imaging 1.2k
  • Artificial Intelligence 839
  • Neurology 506
  • Biomedical Engineering 482
Replace Tal Arbel with:
Tal Arbel Canada
Vishwesh Nath United States
Dong Yang United States
J. Shin United States
Andriy Myronenko United States
Ozan Oktay United Kingdom
Ali Hatamizadeh United States
Michel Bilello United States
Bernhard Kainz United Kingdom
Xin Yang China
Yucheng Tang relative to Tal Arbel Canada Tal Arbel's profile →
Citations per field
00.5×10×15×
Tal Arbel · 1×
Citations per year

Countries citing papers authored by Yucheng Tang

Since Specialization
Citations

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

Fields of papers citing papers by Yucheng Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yucheng Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Yucheng Tang. A scholar is included among the top collaborators of Yucheng Tang 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 Yucheng Tang. Yucheng Tang 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 16
2 1
3 0
4 7
5 2
6 36
7 1
8 9
9 6
10 7
11 11
12 6
13 7
14 70
15 7
16 6
17 1
18 12
19 56
20 42

About Yucheng Tang

Yucheng Tang is a scholar working on Radiology, Nuclear Medicine and Imaging, Health Informatics and Computer Vision and Pattern Recognition, having authored 85 papers that have together received 3.0k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (21 papers), COVID-19 diagnosis using AI (14 papers) and AI in cancer detection (13 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.3k citations), Neurology (506 citations) and Radiology, Nuclear Medicine and Imaging (1.2k citations). Yucheng Tang has collaborated with scholars based in United States, China and Türkiye. Frequent co-authors include Bennett A. Landman, Vishwesh Nath, Daguang Xu, Holger R. Roth, Dong Yang, Ali Hatamizadeh, Andriy Myronenko, Wenqi Li, Hakan Akbulut and Albert Deisseroth. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nature Materials and Blood.

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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