Nancy Diao
Impact in
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in
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- Lung Cancer Diagnosis and Treatment 5
- Lung Cancer Treatments and Mutations 2
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- Heavy Metal Exposure and Toxicity 2
- Co-authors
- David C. Christiani (11 shared papers)Raymond H. Mak (3 shared papers)Andrea T. Shafer (3 shared papers)Hugo J.W.L. Aerts (3 shared papers)Tafadzwa L. Chaunzwa (3 shared papers)Michael Lanuti (3 shared papers)Yiwen Xu (1 shared paper)Ahmed Hosny (1 shared paper)
- Journals
- PLoS ONE (3 papers)Lung Cancer (2 papers)Journal of Clinical Oncology (2 papers)Molecular Human Reproduction (1 paper)Scientific Reports (1 paper)
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Nancy Diao
15 papers receiving 365 citations
Nancy Diao's Hit Papers
Peers
Comparison fields: 5 of 68
- Health Informatics 9
- Radiology, Nuclear Medicine and Imaging 154
- Health, Toxicology and Mutagenesis 70
- Pulmonary and Respiratory Medicine 138
- Artificial Intelligence 94
Countries citing papers authored by Nancy Diao
This map shows the geographic impact of Nancy Diao'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 Nancy Diao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nancy Diao more than expected).
Fields of papers citing papers by Nancy Diao
This network shows the impact of papers produced by Nancy Diao. 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 Nancy Diao. The network helps show where Nancy Diao may publish in the future.
Co-authors
The 25 scholars most cited alongside Nancy Diao, 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 | Deep learning classification of lung cancer histology using CT images Hit paper breakdown → | 2021 | 163 |
| 2 | 2019 | 53 | |
| 3 | 2014 | 44 | |
| 4 | 2014 | 26 | |
| 5 | 2014 | 26 | |
| 6 | 2019 | 12 | |
| 7 | 2021 | 11 | |
| 8 | 2023 | 11 | |
| 9 | 2018 | 11 | |
| 10 | 2012 | 10 | |
| 11 | 2012 | 2 | |
| 12 | 2022 | 2 | |
| 13 | 2018 | 2 | |
| 14 | Association Test Based on SNP Set: Logistic Kernel Machine Based Test vs | 2012 | 1 |
| 15 | 2022 | 1 | |
| 16 | 2016 | 0 |
About Nancy Diao
Nancy Diao is a scholar working on Pulmonary and Respiratory Medicine, Health, Toxicology and Mutagenesis, Molecular Biology, Genetics and Plant Science, having authored 16 papers that have together received 375 indexed citations. Recurring topics across this work include Lung Cancer Diagnosis and Treatment (5 papers), Gene expression and cancer classification (4 papers), Genetic Associations and Epidemiology (3 papers), Pesticide Exposure and Toxicity (3 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Heavy Metal Exposure and Toxicity (2 papers), Lung Cancer Treatments and Mutations (2 papers) and AI in cancer detection (2 papers). The work is most often cited by research in Health Informatics (9 citations), Radiology, Nuclear Medicine and Imaging (154 citations), Health, Toxicology and Mutagenesis (70 citations), Pulmonary and Respiratory Medicine (138 citations) and Artificial Intelligence (94 citations). Nancy Diao has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include David C. Christiani, Raymond H. Mak, Andrea T. Shafer, Hugo J.W.L. Aerts, Tafadzwa L. Chaunzwa, Michael Lanuti, Yiwen Xu, Ahmed Hosny, Maitreyi Mazumdar and Quazi Quamruzzaman. Their work appears in journals such as PLoS ONE, Lung Cancer, Journal of Clinical Oncology, Molecular Human Reproduction and Scientific Reports.
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.