Fei Ren

2.2k citations
29 papers · 771 · h-index 14

Impact in

Papers in

Fei Ren

23 papers receiving 737 citations

Peers

Fei Ren
Comparison fields: 5 of 94
  • Structural Biology 31
  • Artificial Intelligence 505
  • Radiology, Nuclear Medicine and Imaging 279
  • Soil Science 107
  • Health Informatics 13
Replace Matteo Barbieri with:
Matteo Barbieri Italy
Ludovic Roux France
Camille Kurtz France
Xiaowei Chen China
Ryohei Nakayama Japan
Pedro Real Spain
Chen Yao China
Raghav Saboo United States
Johannes C. Paetzold Germany
C. K. Lee Hong Kong
Fei Ren relative to Matteo Barbieri Italy Matteo Barbieri's profile →
Citations per field
00.5×10×15.5×
Matteo Barbieri · 1×
Citations per year

Countries citing papers authored by Fei Ren

Since Specialization
Citations

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

Fields of papers citing papers by Fei Ren

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019242
2 201697
3
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
201890
4
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
201862
5 202150
6 202027
7 202325
8 201721
9 201820
10 201719
11 202216
12 202315
13 201515
14 201913
15 202011
16 201911
17 202211
18 20238
19 20156
20 20184

About Fei Ren

Fei Ren is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Molecular Biology, Computer Vision and Pattern Recognition and Structural Biology, having authored 29 papers that have together received 771 indexed citations. Recurring topics across this work include AI in cancer detection (10 papers), Radiomics and Machine Learning in Medical Imaging (10 papers), Electron and X-Ray Spectroscopy Techniques (3 papers), Advanced Electron Microscopy Techniques and Applications (3 papers), COVID-19 diagnosis using AI (2 papers), Image Retrieval and Classification Techniques (2 papers), Peatlands and Wetlands Ecology (2 papers) and Music and Audio Processing (2 papers). The work is most often cited by research in Structural Biology (31 citations), Artificial Intelligence (505 citations), Radiology, Nuclear Medicine and Imaging (279 citations), Soil Science (107 citations) and Health Informatics (13 citations). Fei Ren has collaborated with scholars based in China, United States and Algeria. Frequent co-authors include Fa Zhang, Rui Yan, Xiaosong Rao, Chun-Hou Zheng, Zihao Wang, Yudong Liu, Lihua Wang, Tong Zhang, Jin He and Huakun Zhou. Their work appears in journals such as Journal of Structural Biology, Diagnostic Pathology, Biogeochemistry, IEEE/ACM Transactions on Computational Biology and Bioinformatics and IEEE Transactions on Pattern Analysis and Machine Intelligence.

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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