Ari Seff
Impact in
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
- Health Informatics top 5%
Papers in
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- AI in cancer detection 5
- Topic Modeling 2
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- COVID-19 diagnosis using AI 4
- Radiomics and Machine Learning in Medical Imaging 3
- Co-authors
- Ronald M. Summers (8 shared papers)Le Lü (7 shared papers)Holger R. Roth (5 shared papers)Kevin M. Cherry (3 shared papers)Jianhua Yao (5 shared papers)Lauren Kim (4 shared papers)Jiamin Liu (2 shared papers)Shijun Wang (2 shared papers)
- Journals
- Journal of Digital Imaging (1 paper)IEEE Transactions on Medical Imaging (1 paper)Brain Imaging and Behavior (1 paper)Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization (1 paper)Lecture notes in computer science (1 paper)
- Partner nations
- United States
In The Last Decade
Ari Seff
10 papers receiving 995 citations
Ari Seff's Hit Papers
Peers
Comparison fields: 5 of 98
- Radiology, Nuclear Medicine and Imaging 511
- Health Informatics 22
- Computer Vision and Pattern Recognition 275
- Artificial Intelligence 381
- Neurology 74
Countries citing papers authored by Ari Seff
This map shows the geographic impact of Ari Seff'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 Ari Seff with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ari Seff more than expected).
Fields of papers citing papers by Ari Seff
This network shows the impact of papers produced by Ari Seff. 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 Ari Seff. The network helps show where Ari Seff may publish in the future.
Co-authors
The 25 scholars most cited alongside Ari Seff, 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 | Improving Computer-Aided Detection UsingConvolutional Neural Networks and Random View Aggregation Hit paper breakdown → | 2015 | 417 |
| 2 | A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations Hit paper breakdown → | 2014 | 287 |
| 3 | 2015 | 116 | |
| 4 | 2015 | 65 | |
| 5 | 2014 | 42 | |
| 6 | 2023 | 42 | |
| 7 | 2016 | 26 | |
| 8 | 2016 | 17 | |
| 9 | 2014 | 15 | |
| 10 | 2015 | 1 |
About Ari Seff
Ari Seff is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Molecular Biology and Automotive Engineering, having authored 10 papers that have together received 1.0k indexed citations. Recurring topics across this work include AI in cancer detection (5 papers), COVID-19 diagnosis using AI (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Medical Image Segmentation Techniques (2 papers), Topic Modeling (2 papers), Multimodal Machine Learning Applications (2 papers), Digital Radiography and Breast Imaging (1 paper) and Advanced X-ray and CT Imaging (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (511 citations), Health Informatics (22 citations), Computer Vision and Pattern Recognition (275 citations), Artificial Intelligence (381 citations) and Neurology (74 citations). Ari Seff has collaborated with scholars based in United States. Frequent co-authors include Ronald M. Summers, Le Lü, Holger R. Roth, Kevin M. Cherry, Jianhua Yao, Lauren Kim, Jiamin Liu, Shijun Wang, Evrim Türkbey and Joanne Hoffman. Their work appears in journals such as Journal of Digital Imaging, IEEE Transactions on Medical Imaging, Brain Imaging and Behavior, Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization and Lecture notes in computer science.
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.