Deepa Sheth
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
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- MRI in cancer diagnosis
- Radiomics and Machine Learning in Medical Imaging
Papers in
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- MRI in cancer diagnosis 14
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- Digital Radiography and Breast Imaging 2
- Co-authors
- Maryellen Lissak Giger (9 shared papers)Hui Li (4 shared papers)Hiroyuki Abé (7 shared papers)Naoko Mori (3 shared papers)Jonathan M. Lorenz (1 shared paper)Jay D. Patel (1 shared paper)Keiko Tsuchiya (2 shared papers)Gregory S. Karczmar (4 shared papers)
- Journals
- Journal of Magnetic Resonance Imaging (3 papers)Breast Cancer Research and Treatment (2 papers)American Journal of Roentgenology (2 papers)Radiology (2 papers)British Journal of Radiology (1 paper)
- Partner nations
- United StatesJapanGermany
In The Last Decade
Deepa Sheth
24 papers receiving 695 citations
Peers
Comparison fields: 5 of 51
- Health Informatics 47
- Radiology, Nuclear Medicine and Imaging 286
- Artificial Intelligence 199
- Cancer Research 50
- Genetics 27
Countries citing papers authored by Deepa Sheth
This map shows the geographic impact of Deepa Sheth'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 Deepa Sheth with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deepa Sheth more than expected).
Fields of papers citing papers by Deepa Sheth
This network shows the impact of papers produced by Deepa Sheth. 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 Deepa Sheth. The network helps show where Deepa Sheth may publish in the future.
Co-authors
The 25 scholars most cited alongside Deepa Sheth, 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 25 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 175 | |
| 2 | 2018 | 91 | |
| 3 | 2019 | 84 | |
| 4 | 2020 | 64 | |
| 5 | 2018 | 48 | |
| 6 | 2021 | 35 | |
| 7 | 2012 | 31 | |
| 8 | 2018 | 30 | |
| 9 | 2017 | 24 | |
| 10 | 2020 | 21 | |
| 11 | 2021 | 20 | |
| 12 | 2017 | 18 | |
| 13 | 2017 | 16 | |
| 14 | 2019 | 15 | |
| 15 | 2021 | 8 | |
| 16 | 2017 | 7 | |
| 17 | 2023 | 6 | |
| 18 | 2020 | 5 | |
| 19 | 2023 | 4 | |
| 20 | 2009 | 3 |
About Deepa Sheth
Deepa Sheth is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Artificial Intelligence, Computer Vision and Pattern Recognition and Genetics, having authored 25 papers that have together received 714 indexed citations. Recurring topics across this work include MRI in cancer diagnosis (14 papers), AI in cancer detection (4 papers), Medical Image Segmentation Techniques (3 papers), Digital Radiography and Breast Imaging (2 papers), Case Reports on Hematomas (1 paper), Global Cancer Incidence and Screening (1 paper), Liver Disease Diagnosis and Treatment (1 paper) and BRCA gene mutations in cancer (1 paper). The work is most often cited by research in Health Informatics (47 citations), Radiology, Nuclear Medicine and Imaging (286 citations), Artificial Intelligence (199 citations), Cancer Research (50 citations) and Genetics (27 citations). Deepa Sheth has collaborated with scholars based in United States, Japan and Germany. Frequent co-authors include Maryellen Lissak Giger, Hui Li, Hiroyuki Abé, Naoko Mori, Jonathan M. Lorenz, Jay D. Patel, Keiko Tsuchiya, Gregory S. Karczmar, David V. Schacht and Kei Takase. Their work appears in journals such as Journal of Magnetic Resonance Imaging, Breast Cancer Research and Treatment, American Journal of Roentgenology, Radiology and British Journal of 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.