Muchao Ye
Impact in
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- Artificial Intelligence in Healthcare
- Artificial Intelligence top 5%
- Machine Learning in Healthcare
- Topic Modeling
- Adversarial Robustness in Machine Learning
Papers in
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- Topic Modeling 9
- Machine Learning in Healthcare 6
- Adversarial Robustness in Machine Learning 3
- Anomaly Detection Techniques and Applications 2
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- Artificial Intelligence in Healthcare 6
- Co-authors
- Fenglong Ma (12 shared papers)Junyu Luo (6 shared papers)Cao Xiao (5 shared papers)Quanzeng You (2 shared papers)Ting Wang (4 shared papers)Chenglin Miao (3 shared papers)Xingyi Yang (1 shared paper)Yaqing Wang (1 shared paper)
- Journals
- 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (2 papers)Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (1 paper)
- Partner nations
- United StatesChinaGermany
In The Last Decade
Muchao Ye
13 papers receiving 279 citations
Peers
Comparison fields: 5 of 47
- Health Information Management 118
- Artificial Intelligence 249
- Health Informatics 8
- Signal Processing 37
- Computer Vision and Pattern Recognition 34
Countries citing papers authored by Muchao Ye
This map shows the geographic impact of Muchao Ye'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 Muchao Ye with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Muchao Ye more than expected).
Fields of papers citing papers by Muchao Ye
This network shows the impact of papers produced by Muchao Ye. 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 Muchao Ye. The network helps show where Muchao Ye may publish in the future.
Co-authors
The 16 scholars most cited alongside Muchao Ye, 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 | 2020 | 121 | |
| 2 | 2021 | 35 | |
| 3 | 2020 | 26 | |
| 4 | 2021 | 26 | |
| 5 | 2022 | 19 | |
| 6 | 2021 | 14 | |
| 7 | 2022 | 11 | |
| 8 | 2021 | 10 | |
| 9 | 2022 | 6 | |
| 10 | 2025 | 5 | |
| 11 | 2023 | 5 | |
| 12 | 2022 | 4 | |
| 13 | 2024 | 1 |
About Muchao Ye
Muchao Ye is a scholar working on Artificial Intelligence, Health Information Management, Computer Vision and Pattern Recognition, Signal Processing and Radiology, Nuclear Medicine and Imaging, having authored 13 papers that have together received 283 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Artificial Intelligence in Healthcare (6 papers), Machine Learning in Healthcare (6 papers), Multimodal Machine Learning Applications (4 papers), Adversarial Robustness in Machine Learning (3 papers), Anomaly Detection Techniques and Applications (2 papers), Human Pose and Action Recognition (1 paper) and COVID-19 diagnosis using AI (1 paper). The work is most often cited by research in Health Information Management (118 citations), Artificial Intelligence (249 citations), Health Informatics (8 citations), Signal Processing (37 citations) and Computer Vision and Pattern Recognition (34 citations). Muchao Ye has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Fenglong Ma, Junyu Luo, Cao Xiao, Quanzeng You, Ting Wang, Chenglin Miao, Xingyi Yang, Yaqing Wang, Jinghui Chen and Weiyang Liu. Their work appears in journals such as 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Proceedings of the AAAI Conference on Artificial Intelligence and Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
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.