Yixuan Yuan
- Gastroenterology top 1%
- Gastrointestinal Bleeding Diagnosis and Treatment 17
-
- Advanced Neural Network Applications 34
- Multimodal Machine Learning Applications 14
-
- Radiomics and Machine Learning in Medical Imaging 27
- COVID-19 diagnosis using AI 20
- Health Informatics top 2%
- Artificial Intelligence top 0.5%
- Domain Adaptation and Few-Shot Learning 30
- AI in cancer detection 25
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- Colorectal Cancer Screening and Detection 12
- Cited by
- GastroenterologyComputer Vision and Pattern RecognitionRadiology, Nuclear Medicine and Imaging
- Journals
- Nature (1 paper)SHILAP Revista de lepidopterología (2 papers)The Astrophysical Journal (1 paper)
- Partner nations
- Hong KongChinaUnited States
In The Last Decade
Yixuan Yuan
160 papers receiving 4.6k citations
Hit Papers
Peers
Comparison fields: 5 of 165
- Gastroenterology 538
- Computer Vision and Pattern Recognition 1.8k
- Radiology, Nuclear Medicine and Imaging 1.3k
- Health Informatics 70
- Artificial Intelligence 1.5k
Countries citing papers authored by Yixuan Yuan
This map shows the geographic impact of Yixuan Yuan'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 Yixuan Yuan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yixuan Yuan more than expected).
Fields of papers citing papers by Yixuan Yuan
This network shows the impact of papers produced by Yixuan Yuan. 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 Yixuan Yuan. The network helps show where Yixuan Yuan may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Yixuan Yuan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 7 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2024 | 1 | |
| 5 | Causal Disentanglement Domain Generalization for time-series signal fault diagnosisbreakdown → | 2024 | 48 |
| 6 | 2024 | 3 | |
| 7 | 2024 | 1 | |
| 8 | 2024 | 5 | |
| 9 | 2024 | 0 | |
| 10 | 2023 | 13 | |
| 11 | 2023 | 15 | |
| 12 | GTFE-Net: A Gramian Time Frequency Enhancement CNN for bearing fault diagnosisbreakdown → | 2023 | 111 |
| 13 | 2023 | 12 | |
| 14 | 2023 | 6 | |
| 15 | 2023 | 17 | |
| 16 | 2023 | 4 | |
| 17 | 2022 | 98 | |
| 18 | 2021 | 8 | |
| 19 | 2021 | 26 | |
| 20 | 2020 | 53 |
About Yixuan Yuan
Yixuan Yuan is a scholar working on Computer Vision and Pattern Recognition, Gastroenterology and Health Informatics, having authored 180 papers that have together received 4.7k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (34 papers), Domain Adaptation and Few-Shot Learning (30 papers), Radiomics and Machine Learning in Medical Imaging (27 papers), AI in cancer detection (25 papers), COVID-19 diagnosis using AI (20 papers), Gastrointestinal Bleeding Diagnosis and Treatment (17 papers), Multimodal Machine Learning Applications (14 papers) and Colorectal Cancer Screening and Detection (12 papers). The work is most often cited by research in Gastroenterology (538 citations), Computer Vision and Pattern Recognition (1.8k citations) and Radiology, Nuclear Medicine and Imaging (1.3k citations). Yixuan Yuan has collaborated with scholars based in Hong Kong, China and United States. Frequent co-authors include Max Q.‐H. Meng, Xiaoqing Guo, Baopu Li, Wuyang Li, Zhen Chen, Xinyu Liu, Bulat Ibragimov, Lei Xing, Chen Yang and Tommy W. S. Chow. Their work appears in journals such as Nature, SHILAP Revista de lepidopterología and The Astrophysical Journal.
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.