Jason Causey
Impact in
-
- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in
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- COVID-19 diagnosis using AI 2
- Radiomics and Machine Learning in Medical Imaging 2
- Genetics 3
- Co-authors
- Xiuzhen Huang (9 shared papers)Shiqian Ma (2 shared papers)Shuzhong Zhang (2 shared papers)David G. Politte (1 shared paper)Fred Prior (1 shared paper)Junyu Zhang (1 shared paper)Bo Jiang (1 shared paper)Yuanfang Guan (3 shared papers)
- Journals
- Scientific Reports (5 papers)IEEE Transactions on Computational Biology and Bioinformatics (2 papers)JAMA Network Open (1 paper)Frontiers in Plant Science (1 paper)Genetics (1 paper)
- Partner nations
- United StatesChinaAustralia
In The Last Decade
Jason Causey
13 papers receiving 315 citations
Peers
Comparison fields: 5 of 61
- Radiology, Nuclear Medicine and Imaging 167
- Health Informatics 8
- Pulmonary and Respiratory Medicine 131
- Health Information Management 10
- Artificial Intelligence 72
Countries citing papers authored by Jason Causey
This map shows the geographic impact of Jason Causey'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 Jason Causey with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jason Causey more than expected).
Fields of papers citing papers by Jason Causey
This network shows the impact of papers produced by Jason Causey. 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 Jason Causey. The network helps show where Jason Causey may publish in the future.
Co-authors
The 25 scholars most cited alongside Jason Causey, 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 | 2018 | 169 | |
| 2 | 2020 | 29 | |
| 3 | 2022 | 28 | |
| 4 | 2021 | 25 | |
| 5 | 2018 | 15 | |
| 6 | 2021 | 13 | |
| 7 | 2020 | 11 | |
| 8 | 2024 | 11 | |
| 9 | 2016 | 9 | |
| 10 | 2023 | 6 | |
| 11 | 2024 | 3 | |
| 12 | 2009 | 2 | |
| 13 | Completing College 2020: A National View of Student Completion Rates for 2014 Entering Cohort. (Signature Report No. 19). | 2020 | 1 |
| 14 | 2024 | 1 | |
| 15 | Current Term Enrollment Estimates: Spring 2020. A COVID-19 Supplement: With New Data Submitted in April and May 2020. | 2020 | 0 |
About Jason Causey
Jason Causey is a scholar working on Radiology, Nuclear Medicine and Imaging, Genetics, Pulmonary and Respiratory Medicine, Computer Vision and Pattern Recognition and Ecology, having authored 15 papers that have together received 323 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (2 papers), Lung Cancer Diagnosis and Treatment (2 papers), Gene expression and cancer classification (2 papers), Remote Sensing in Agriculture (2 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Crop Yield and Soil Fertility (1 paper), Cancer Genomics and Diagnostics (1 paper) and Genomics and Phylogenetic Studies (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (167 citations), Health Informatics (8 citations), Pulmonary and Respiratory Medicine (131 citations), Health Information Management (10 citations) and Artificial Intelligence (72 citations). Jason Causey has collaborated with scholars based in United States, China and Australia. Frequent co-authors include Xiuzhen Huang, Shiqian Ma, Shuzhong Zhang, David G. Politte, Fred Prior, Junyu Zhang, Bo Jiang, Yuanfang Guan, Jason H. Moore and Wei Dong. Their work appears in journals such as Scientific Reports, IEEE Transactions on Computational Biology and Bioinformatics, JAMA Network Open, Frontiers in Plant Science and Genetics.
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.