Yin Dai

713 citations
14 papers · 455 indexed · 1 hit paper · h-index 9
Topics
Radiomics and Machine Learning in Medical Imaging (4 papers)Image and Signal Denoising Methods (3 papers)Advanced Image Fusion Techniques (3 papers)
Partner nations
ChinaUnited Kingdom

In The Last Decade

Yin Dai

14 papers receiving 443 citations

Hit Papers

TransMed: Transformers Advance Multi-Modal Medical Image ...2021202620222024202150100150200250

Peers

Yin Dai
Comparison fields: 5 of 80
  • Computer Vision and Pattern Recognition 214
  • Artificial Intelligence 162
  • Radiology, Nuclear Medicine and Imaging 156
  • Neurology 78
  • Biomedical Engineering 62
Replace Haseeb Hassan with:
Haseeb Hassan China
K. Balasamy India
V. Seethalakshmi India
Tianyu Shi China
Yeqi Bai Singapore
Arnaldo Mayer Israel
Bibo Shi United States
Irena Galić Croatia
Ivan Cruz‐Aceves Mexico
Yanda Meng United Kingdom
Yin Dai relative to Haseeb Hassan China Haseeb Hassan's profile →
Citations per field
00.5×3.4×
Haseeb Hassan · 1×
Citations per year

Countries citing papers authored by Yin Dai

Since Specialization
Citations

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

Fields of papers citing papers by Yin Dai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yin Dai

This figure shows the co-authorship network connecting the top 25 collaborators of Yin Dai. A scholar is included among the top collaborators of Yin Dai 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 Yin Dai. Yin Dai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

14 of 14 papers shown
#WorkIndexed citations
1 3
2 3
3 12
4 5
5 22
6 17
7 7
8
TransMed: Transformers Advance Multi-Modal Medical Image Classificationbreakdown →
260
9 26
10 9
11 22
12 3
13 35
14 31

About Yin Dai

Yin Dai is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Radiology, Nuclear Medicine and Imaging, having authored 14 papers that have together received 455 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (4 papers), Image and Signal Denoising Methods (3 papers) and Advanced Image Fusion Techniques (3 papers). The work is most often cited by research in Health Informatics (18 citations), Computer Vision and Pattern Recognition (214 citations) and Neurology (78 citations). Yin Dai has collaborated with scholars based in China and United Kingdom. Frequent co-authors include Yifan Gao, Fayu Liu, Xin Wang, Yuyi Wang, Yang Wang, Zheng Tang, Weibing Chen, Weibin Liu, Fayu Liu and Yue Liu. Their work appears in journals such as IEEE Access, Medical Physics and Applied Sciences.

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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