Long Yu
Impact in
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- Advanced Neural Network Applications
- Medical Image Segmentation Techniques
- Media Technology top 2%
Papers in
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- AI in cancer detection 22
- Natural Language Processing Techniques 14
- Topic Modeling 13
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- Advanced Neural Network Applications 21
- Medical Image Segmentation Techniques 12
- Image Enhancement Techniques 9
- Co-authors
- Shengwei Tian (84 shared papers)Shengwei Tian (10 shared papers)Weidong Wu (12 shared papers)Xiang Ma (5 shared papers)Xinjun Pei (3 shared papers)Hanli Wang (2 shared papers)Junlong Cheng (6 shared papers)Aolun Li (6 shared papers)
In The Last Decade
Long Yu
134 papers receiving 1.8k citations
Long Yu's Hit Papers
Peers
Comparison fields: 5 of 159
- Computer Vision and Pattern Recognition 565
- Media Technology 171
- Artificial Intelligence 613
- Neurology 114
- Signal Processing 153
Countries citing papers authored by Long Yu
This map shows the geographic impact of Long Yu'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 Long Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Long Yu more than expected).
Fields of papers citing papers by Long Yu
This network shows the impact of papers produced by Long Yu. 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 Long Yu. The network helps show where Long Yu may publish in the future.
Co-authors
The 25 scholars most cited alongside Long Yu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 153 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | HiFuse: Hierarchical multi-scale feature fusion network for medical image classification Hit paper breakdown → | 2023 | 151 |
| 2 | 2021 | 150 | |
| 3 | 2021 | 87 | |
| 4 | 2019 | 86 | |
| 5 | 2017 | 84 | |
| 6 | 2020 | 80 | |
| 7 | 2021 | 58 | |
| 8 | 2018 | 56 | |
| 9 | 2013 | 49 | |
| 10 | 2021 | 47 | |
| 11 | 2011 | 47 | |
| 12 | 2022 | 35 | |
| 13 | 2022 | 34 | |
| 14 | 2020 | 28 | |
| 15 | 2022 | 28 | |
| 16 | 2022 | 26 | |
| 17 | 2023 | 26 | |
| 18 | 2018 | 23 | |
| 19 | 2023 | 22 | |
| 20 | 2020 | 21 |
About Long Yu
Long Yu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology, Oncology and Radiology, Nuclear Medicine and Imaging, having authored 153 papers that have together received 1.8k indexed citations. Recurring topics across this work include AI in cancer detection (22 papers), Advanced Neural Network Applications (21 papers), Natural Language Processing Techniques (14 papers), Topic Modeling (13 papers), Medical Image Segmentation Techniques (12 papers), Cutaneous Melanoma Detection and Management (11 papers), Image Enhancement Techniques (9 papers) and Radiomics and Machine Learning in Medical Imaging (9 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (565 citations), Media Technology (171 citations), Artificial Intelligence (613 citations), Neurology (114 citations) and Signal Processing (153 citations). Long Yu has collaborated with scholars based in China, Hong Kong and Spain. Frequent co-authors include Shengwei Tian, Shengwei Tian, Weidong Wu, Xiang Ma, Xinjun Pei, Hanli Wang, Junlong Cheng, Aolun Li, Xiaojing Kang and Hongchun Lu. Their work appears in journals such as Journal of Intelligent & Fuzzy Systems, Biomedical Signal Processing and Control, Multimedia Tools and Applications, Complex & Intelligent Systems and Engineering Applications of Artificial 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.