Phillip M. Cheng
- Radiology, Nuclear Medicine and Imaging top 1%
- Biomedical Engineering top 10%
- Artificial Intelligence top 5%
- Pulmonary and Respiratory Medicine top 10%
- Health Informatics top 0.2%
- Co-authors
- An TangGabriel ChartrandSamuel KadouryHarshawn MalhiChristopher PalMichal DrozdzalSimon TurcotteEugene Vorontsov
- Topics
- Radiomics and Machine Learning in Medical Imaging (13 papers)Advanced X-ray and CT Imaging (8 papers)Medical Imaging Techniques and Applications (6 papers)
- Journals
- SHILAP Revista de lepidopterologíaJournal of NeurophysiologyThe American Journal of Cardiology
- Partner nations
- United StatesCanadaFrance
In The Last Decade
Phillip M. Cheng
32 papers receiving 1.7k citations
Hit Papers
Peers
Comparison fields: 5 of 143
- Radiology, Nuclear Medicine and Imaging 990
- Biomedical Engineering 402
- Artificial Intelligence 379
- Pulmonary and Respiratory Medicine 308
- Health Informatics 283
Countries citing papers authored by Phillip M. Cheng
This map shows the geographic impact of Phillip M. Cheng'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 Phillip M. Cheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Phillip M. Cheng more than expected).
Fields of papers citing papers by Phillip M. Cheng
This network shows the impact of papers produced by Phillip M. Cheng. 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 Phillip M. Cheng. The network helps show where Phillip M. Cheng may publish in the future.
Co-authorship network of co-authors of Phillip M. Cheng
This figure shows the co-authorship network connecting the top 25 collaborators of Phillip M. Cheng. A scholar is included among the top collaborators of Phillip M. Cheng based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Phillip M. Cheng. Phillip M. Cheng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 9 | |
| 6 | 105 | |
| 7 | 3 | |
| 8 | 34 | |
| 9 | Deep Learning: A Primer for Radiologistsbreakdown → | 788 |
| 10 | 199 | |
| 11 | 13 | |
| 12 | 38 | |
| 13 | 8 | |
| 14 | 10 | |
| 15 | 18 | |
| 16 | 21 | |
| 17 | 3 | |
| 18 | 55 | |
| 19 | 69 | |
| 20 | Use of the Genetic Algorithm for Load Balancing of Sugar Beet Presses | 16 |
About Phillip M. Cheng
Phillip M. Cheng is a scholar working on Radiology, Nuclear Medicine and Imaging, Health Informatics and Conservation, having authored 34 papers that have together received 1.7k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (13 papers), Advanced X-ray and CT Imaging (8 papers) and Medical Imaging Techniques and Applications (6 papers). The work is most often cited by research in Health Informatics (283 citations), Radiology, Nuclear Medicine and Imaging (990 citations) and Artificial Intelligence (379 citations). Phillip M. Cheng has collaborated with scholars based in United States, Canada and France. Frequent co-authors include An Tang, Gabriel Chartrand, Samuel Kadoury, Harshawn Malhi, Christopher Pal, Michal Drozdzal, Simon Turcotte, Eugene Vorontsov, Joseph R. England and Alexandre Cadrin-Chênevert. Their work appears in journals such as SHILAP Revista de lepidopterología, Journal of Neurophysiology and The American Journal of Cardiology.
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.