Daniel L. Rubin
Impact in
- Health Informatics top 0.02%
- Artificial Intelligence in Healthcare and Education
- Radiology, Nuclear Medicine and Imaging top 0.05%
- Radiomics and Machine Learning in Medical Imaging
- Retinal Imaging and Analysis
- COVID-19 diagnosis using AI
Papers in
-
- Radiomics and Machine Learning in Medical Imaging 86
- Medical Imaging Techniques and Applications 29
- Radiology practices and education 26
- Retinal Imaging and Analysis 24
- MRI in cancer diagnosis 19
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- AI in cancer detection 65
- Semantic Web and Ontologies 32
- Co-authors
- Assaf Hoogi (13 shared papers)Sandy Napel (33 shared papers)Luís de Sisternes (27 shared papers)Alfiia Galimzianova (6 shared papers)Gerald J. Berry (5 shared papers)Russ B. Altman (11 shared papers)Mark A. Musen (22 shared papers)Bradley J. Erickson (2 shared papers)
- Journals
- Journal of Digital Imaging (24 papers)Radiology (15 papers)Journal of Biomedical Informatics (14 papers)Journal of the American Medical Informatics Association (9 papers)Investigative Radiology (9 papers)
- Partner nations
- United StatesChinaCanada
In The Last Decade
Daniel L. Rubin
363 papers receiving 15.9k citations
Daniel L. Rubin's Hit Papers
Peers
Comparison fields: 5 of 207
- Health Informatics 1.0k
- Radiology, Nuclear Medicine and Imaging 6.6k
- Artificial Intelligence 5.1k
- Ophthalmology 957
- Neurology 809
Countries citing papers authored by Daniel L. Rubin
This map shows the geographic impact of Daniel L. Rubin'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 Daniel L. Rubin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel L. Rubin more than expected).
Fields of papers citing papers by Daniel L. Rubin
This network shows the impact of papers produced by Daniel L. Rubin. 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 Daniel L. Rubin. The network helps show where Daniel L. Rubin may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel L. Rubin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 370 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions Hit paper breakdown → | 2017 | 739 |
| 2 | Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features Hit paper breakdown → | 2016 | 692 |
| 3 | Preparing Medical Imaging Data for Machine Learning Hit paper breakdown → | 2020 | 580 |
| 4 | BioPortal: ontologies and integrated data resources at the click of a mouse Hit paper breakdown → | 2009 | 562 |
| 5 | A curated mammography data set for use in computer-aided detection and diagnosis research Hit paper breakdown → | 2017 | 512 |
| 6 | 2012 | 318 | |
| 7 | 2001 | 255 | |
| 8 | Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study Hit paper breakdown → | 2020 | 252 |
| 9 | 2010 | 246 | |
| 10 | 2009 | 229 | |
| 11 | Differential Data Augmentation Techniques for Medical Imaging Classification Tasks. | 2017 | 218 |
| 12 | 2018 | 217 | |
| 13 | 2018 | 216 | |
| 14 | 2018 | 204 | |
| 15 | 2015 | 199 | |
| 16 | 2018 | 194 | |
| 17 | Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks. | 2015 | 193 |
| 18 | 2007 | 181 | |
| 19 | 2009 | 173 | |
| 20 | 2016 | 173 |
About Daniel L. Rubin
Daniel L. Rubin is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Molecular Biology, Pulmonary and Respiratory Medicine and Computer Vision and Pattern Recognition, having authored 370 papers that have together received 16.4k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (86 papers), Biomedical Text Mining and Ontologies (84 papers), AI in cancer detection (65 papers), Semantic Web and Ontologies (32 papers), Medical Imaging Techniques and Applications (29 papers), Radiology practices and education (26 papers), Retinal Imaging and Analysis (24 papers) and MRI in cancer diagnosis (19 papers). The work is most often cited by research in Health Informatics (1.0k citations), Radiology, Nuclear Medicine and Imaging (6.6k citations), Artificial Intelligence (5.1k citations), Ophthalmology (957 citations) and Neurology (809 citations). Daniel L. Rubin has collaborated with scholars based in United States, China and Canada. Frequent co-authors include Assaf Hoogi, Sandy Napel, Luís de Sisternes, Alfiia Galimzianova, Gerald J. Berry, Russ B. Altman, Mark A. Musen, Bradley J. Erickson, Theodore Leng and Christopher Ré. Their work appears in journals such as Journal of Digital Imaging, Radiology, Journal of Biomedical Informatics, Journal of the American Medical Informatics Association and Investigative Radiology.
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.