Gongbo Liang
- Health Informatics top 5%
- Neurology top 10%
- Brain Tumor Detection and Classification 6
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- Radiomics and Machine Learning in Medical Imaging 7
- COVID-19 diagnosis using AI 3
- Artificial Intelligence top 5%
- AI in cancer detection 10
- Adversarial Robustness in Machine Learning 5
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- Advanced Neural Network Applications 5
- Medical Image Segmentation Techniques 3
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- Medical Imaging and Analysis 4
- Co-authors
- Nathan JacobsXiaoqin WangYu ZhangXin XingAi‐Ling LinZachary BessingerLiangliang LiuTyler C. Hammond
- Journals
- Monthly Notices of the Royal Astronomical Society (2 papers)IEEE Access (1 paper)Neurocomputing (1 paper)
- Partner nations
- United StatesChinaCanada
In The Last Decade
Gongbo Liang
32 papers receiving 469 citations
Peers
Comparison fields: 5 of 102
- Health Informatics 39
- Neurology 75
- Radiology, Nuclear Medicine and Imaging 180
- Artificial Intelligence 232
- Computer Vision and Pattern Recognition 81
Countries citing papers authored by Gongbo Liang
This map shows the geographic impact of Gongbo Liang'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 Gongbo Liang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gongbo Liang more than expected).
Fields of papers citing papers by Gongbo Liang
This network shows the impact of papers produced by Gongbo Liang. 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 Gongbo Liang. The network helps show where Gongbo Liang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Gongbo Liang, 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 | 2024 | 3 | |
| 2 | 2024 | 3 | |
| 3 | 2023 | 16 | |
| 4 | 2023 | 10 | |
| 5 | 2023 | 4 | |
| 6 | 2023 | 2 | |
| 7 | 2023 | 7 | |
| 8 | 2022 | 30 | |
| 9 | 2022 | 11 | |
| 10 | 2022 | 4 | |
| 11 | 2022 | 5 | |
| 12 | 2022 | 0 | |
| 13 | β-amyloid and tau drive early Alzheimer’s disease decline while glucose hypometabolism drives late decline | 2020 | 2 |
| 14 | 2020 | 2 | |
| 15 | 2020 | 82 | |
| 16 | 2020 | 19 | |
| 17 | 2020 | 106 | |
| 18 | 2020 | 17 | |
| 19 | 2019 | 25 | |
| 20 | 2019 | 18 |
About Gongbo Liang
Gongbo Liang is a scholar working on Neurology, Health Informatics and Computer Vision and Pattern Recognition, having authored 38 papers that have together received 479 indexed citations. Recurring topics across this work include AI in cancer detection (10 papers), Radiomics and Machine Learning in Medical Imaging (7 papers), Brain Tumor Detection and Classification (6 papers), Adversarial Robustness in Machine Learning (5 papers), Advanced Neural Network Applications (5 papers), Medical Imaging and Analysis (4 papers), Medical Image Segmentation Techniques (3 papers) and COVID-19 diagnosis using AI (3 papers). The work is most often cited by research in Health Informatics (39 citations), Neurology (75 citations) and Radiology, Nuclear Medicine and Imaging (180 citations). Gongbo Liang has collaborated with scholars based in United States, China and Canada. Frequent co-authors include Nathan Jacobs, Xiaoqin Wang, Yu Zhang, Xin Xing, Ai‐Ling Lin, Zachary Bessinger, Liangliang Liu, Tyler C. Hammond, Kwangsik Nho and Paul K. Crane. Their work appears in journals such as Monthly Notices of the Royal Astronomical Society, IEEE Access and Neurocomputing.
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.