Lily H. Peng
- Artificial Intelligence top 5%
- Radiology, Nuclear Medicine and Imaging top 5%
- Pulmonary and Respiratory Medicine
- Health Informatics top 1%
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Yun LiuNiels OlsonMartin C. StumpeJason HippJenny L. SmithArash MohtashamianMohammad NorouziGeorge E. Dahl
- Topics
- Radiomics and Machine Learning in Medical Imaging (5 papers)Colorectal Cancer Screening and Detection (3 papers)AI in cancer detection (3 papers)
- Journals
- SHILAP Revista de lepidopterologíaJAMA Network OpenArchives of Pathology & Laboratory Medicine
- Partner nations
- United StatesAustriaUnited Kingdom
In The Last Decade
Lily H. Peng
6 papers receiving 670 citations
Hit Papers
Peers
Comparison fields: 5 of 91
- Artificial Intelligence 446
- Radiology, Nuclear Medicine and Imaging 376
- Pulmonary and Respiratory Medicine 134
- Health Informatics 129
- Computer Vision and Pattern Recognition 96
Countries citing papers authored by Lily H. Peng
This map shows the geographic impact of Lily H. Peng'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 Lily H. Peng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lily H. Peng more than expected).
Fields of papers citing papers by Lily H. Peng
This network shows the impact of papers produced by Lily H. Peng. 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 Lily H. Peng. The network helps show where Lily H. Peng may publish in the future.
Co-authorship network of co-authors of Lily H. Peng
This figure shows the co-authorship network connecting the top 25 collaborators of Lily H. Peng. A scholar is included among the top collaborators of Lily H. Peng 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 Lily H. Peng. Lily H. Peng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 12 | |
| 2 | 31 | |
| 3 | 86 | |
| 4 | 12 | |
| 5 | Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancerbreakdown → | 302 |
| 6 | 246 |
About Lily H. Peng
Lily H. Peng is a scholar working on Radiology, Nuclear Medicine and Imaging, Ophthalmology and Oncology, having authored 6 papers that have together received 689 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (5 papers), Colorectal Cancer Screening and Detection (3 papers) and AI in cancer detection (3 papers). The work is most often cited by research in Health Informatics (129 citations), Radiology, Nuclear Medicine and Imaging (376 citations) and Artificial Intelligence (446 citations). Lily H. Peng has collaborated with scholars based in United States, Austria and United Kingdom. Frequent co-authors include Yun Liu, Niels Olson, Martin C. Stumpe, Jason Hipp, Jenny L. Smith, Arash Mohtashamian, Mohammad Norouzi, George E. Dahl, Timo Kohlberger and Greg S. Corrado. Their work appears in journals such as SHILAP Revista de lepidopterología, JAMA Network Open and Archives of Pathology & Laboratory Medicine.
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.