Mo Li
Impact in
- Neurology top 10%
- Brain Tumor Detection and Classification
- Artificial Intelligence top 5%
- AI in cancer detection
Papers in
-
- Advanced Graph Neural Networks 4
- AI in cancer detection 3
-
- Video Coding and Compression Technologies 5
- Data Management and Algorithms 4
- Co-authors
- Yudong Yao (2 shared papers)Junchang Xin (4 shared papers)Huaxia Wang (1 shared paper)Hanyu Jiang (1 shared paper)Hao Zhang (1 shared paper)Zhiqiong Wang (2 shared papers)Zhou Xiao-min (1 shared paper)Md Mamunur Rahaman (1 shared paper)
- Journals
- Knowledge-Based Systems (2 papers)IEEE Access (2 papers)Agricultural Water Management (1 paper)Agricultural Systems (1 paper)Journal of X-Ray Science and Technology (1 paper)
- Partner nations
- ChinaAustraliaUnited States
In The Last Decade
Mo Li
18 papers receiving 443 citations
Peers
Comparison fields: 5 of 63
- Neurology 97
- Artificial Intelligence 331
- Radiology, Nuclear Medicine and Imaging 209
- Health Informatics 9
- Computer Vision and Pattern Recognition 142
Countries citing papers authored by Mo Li
This map shows the geographic impact of Mo Li'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 Mo Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mo Li more than expected).
Fields of papers citing papers by Mo Li
This network shows the impact of papers produced by Mo Li. 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 Mo Li. The network helps show where Mo Li may publish in the future.
Co-authors
The 25 scholars most cited alongside Mo Li, 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 25 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 243 | |
| 2 | 2020 | 148 | |
| 3 | 2005 | 28 | |
| 4 | 2024 | 15 | |
| 5 | 2014 | 10 | |
| 6 | 2020 | 3 | |
| 7 | 2019 | 3 | |
| 8 | 2006 | 3 | |
| 9 | 2006 | 3 | |
| 10 | 2024 | 2 | |
| 11 | 2006 | 2 | |
| 12 | Application of web-based data mining technology in digital libraries | 2007 | 1 |
| 13 | 2022 | 1 | |
| 14 | 2022 | 1 | |
| 15 | 2018 | 1 | |
| 16 | 2006 | 1 | |
| 17 | 2025 | 1 | |
| 18 | 2025 | 1 | |
| 19 | 2018 | 1 | |
| 20 | 2024 | 1 |
About Mo Li
Mo Li is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Computer Networks and Communications and Statistical and Nonlinear Physics, having authored 25 papers that have together received 469 indexed citations. Recurring topics across this work include Video Coding and Compression Technologies (5 papers), Data Management and Algorithms (4 papers), Advanced Graph Neural Networks (4 papers), Image and Video Quality Assessment (4 papers), Advanced Data Compression Techniques (3 papers), Complex Network Analysis Techniques (3 papers), AI in cancer detection (3 papers) and Water-Energy-Food Nexus Studies (2 papers). The work is most often cited by research in Neurology (97 citations), Artificial Intelligence (331 citations), Radiology, Nuclear Medicine and Imaging (209 citations), Health Informatics (9 citations) and Computer Vision and Pattern Recognition (142 citations). Mo Li has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Yudong Yao, Junchang Xin, Huaxia Wang, Hanyu Jiang, Hao Zhang, Zhiqiong Wang, Zhou Xiao-min, Md Mamunur Rahaman, Chen Li and Dan Xue. Their work appears in journals such as Knowledge-Based Systems, IEEE Access, Agricultural Water Management, Agricultural Systems and Journal of X-Ray Science and Technology.
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.