Chin-Fu Liu
Impact in
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- Brain Tumor Detection and Classification
Papers in
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- Advanced Neuroimaging Techniques and Applications 4
- Advanced MRI Techniques and Applications 2
- Co-authors
- Michael I. Miller (8 shared papers)Andréia V. Faria (8 shared papers)J. Tilak Ratnanather (4 shared papers)Argye E. Hillis (3 shared papers)Johnny Hsu (2 shared papers)Erin L. Meier (1 shared paper)Shannon M. Sheppard (1 shared paper)Xin Xu (2 shared papers)
- Journals
- NeuroImage Clinical (2 papers)Scientific Data (2 papers)Computer Communications (1 paper)Biometrics (1 paper)Brain Communications (1 paper)
- Partner nations
- United StatesAustraliaUnited Kingdom
In The Last Decade
Chin-Fu Liu
11 papers receiving 162 citations
Peers
Comparison fields: 5 of 55
- Neurology 37
- Health Information Management 10
- Psychiatry and Mental health 29
- Radiology, Nuclear Medicine and Imaging 37
- Computational Mathematics 1
Countries citing papers authored by Chin-Fu Liu
This map shows the geographic impact of Chin-Fu Liu'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 Chin-Fu Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chin-Fu Liu more than expected).
Fields of papers citing papers by Chin-Fu Liu
This network shows the impact of papers produced by Chin-Fu Liu. 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 Chin-Fu Liu. The network helps show where Chin-Fu Liu may publish in the future.
Co-authors
The 25 scholars most cited alongside Chin-Fu Liu, 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 | 2019 | 51 | |
| 2 | 2023 | 43 | |
| 3 | 2018 | 36 | |
| 4 | 2023 | 15 | |
| 5 | 2023 | 9 | |
| 6 | 2023 | 2 | |
| 7 | 2004 | 2 | |
| 8 | 2023 | 1 | |
| 9 | 2022 | 1 | |
| 10 | 2022 | 1 | |
| 11 | 2021 | 1 |
About Chin-Fu Liu
Chin-Fu Liu is a scholar working on Radiology, Nuclear Medicine and Imaging, Molecular Biology, Cellular and Molecular Neuroscience, Neurology and Pulmonary and Respiratory Medicine, having authored 11 papers that have together received 162 indexed citations. Recurring topics across this work include Advanced Neuroimaging Techniques and Applications (4 papers), Neurological disorders and treatments (2 papers), Dementia and Cognitive Impairment Research (2 papers), Genetic Neurodegenerative Diseases (2 papers), Advanced MRI Techniques and Applications (2 papers), Cerebrovascular and Carotid Artery Diseases (2 papers), Functional Brain Connectivity Studies (2 papers) and Acute Ischemic Stroke Management (2 papers). The work is most often cited by research in Neurology (37 citations), Health Information Management (10 citations), Psychiatry and Mental health (29 citations), Radiology, Nuclear Medicine and Imaging (37 citations) and Computational Mathematics (1 citation). Chin-Fu Liu has collaborated with scholars based in United States, Australia and United Kingdom. Frequent co-authors include Michael I. Miller, Andréia V. Faria, J. Tilak Ratnanather, Argye E. Hillis, Johnny Hsu, Erin L. Meier, Shannon M. Sheppard, Xin Xu, Shreyas Padhy and Laurent Younès. Their work appears in journals such as NeuroImage Clinical, Scientific Data, Computer Communications, Biometrics and Brain Communications.
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.