Shekoofeh Azizi
- Health Informatics top 2%
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- Image and Signal Denoising Methods 4
- Advanced Steganography and Watermarking Techniques 4
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- Ultrasound Imaging and Elastography 4
- Radiomics and Machine Learning in Medical Imaging 2
- Artificial Intelligence top 5%
- AI in cancer detection 9
- Domain Adaptation and Few-Shot Learning 3
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- Prostate Cancer Diagnosis and Treatment 5
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- Advanced Image Fusion Techniques 3
- Co-authors
- Qiang TangZili YiDaesik JangZhan XuMohammad NorouziSimon KornblithAlan KarthikesalingamFiona Ryan
- Cited by
- Health InformaticsComputer Vision and Pattern RecognitionRadiology, Nuclear Medicine and Imaging
- Journals
- Nature Medicine (1 paper)SHILAP Revista de lepidopterología (1 paper)Nature reviews. Cancer (1 paper)
- Partner nations
- United StatesCanadaSouth Korea
In The Last Decade
Shekoofeh Azizi
24 papers receiving 849 citations
Hit Papers
Peers
Comparison fields: 5 of 115
- Health Informatics 64
- Computer Vision and Pattern Recognition 362
- Radiology, Nuclear Medicine and Imaging 333
- Computer Graphics and Computer-Aided Design 39
- Artificial Intelligence 350
Countries citing papers authored by Shekoofeh Azizi
This map shows the geographic impact of Shekoofeh Azizi'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 Shekoofeh Azizi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shekoofeh Azizi more than expected).
Fields of papers citing papers by Shekoofeh Azizi
This network shows the impact of papers produced by Shekoofeh Azizi. 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 Shekoofeh Azizi. The network helps show where Shekoofeh Azizi may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Shekoofeh Azizi, 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 | 2026 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2024 | 6 | |
| 4 | Generative models improve fairness of medical classifiers under distribution shiftsbreakdown → | 2024 | 55 |
| 5 | 2023 | 12 | |
| 6 | 2023 | 1 | |
| 7 | 2020 | 238 | |
| 8 | 2019 | 14 | |
| 9 | 2018 | 9 | |
| 10 | 2018 | 3 | |
| 11 | 2018 | 64 | |
| 12 | 2017 | 23 | |
| 13 | 2017 | 36 | |
| 14 | 2017 | 16 | |
| 15 | 2016 | 34 | |
| 16 | 2014 | 4 | |
| 17 | 2013 | 5 | |
| 18 | 2013 | 2 | |
| 19 | 2002 | 6 | |
| 20 | 1999 | 1 |
About Shekoofeh Azizi
Shekoofeh Azizi is a scholar working on Health Informatics, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 25 papers that have together received 876 indexed citations. Recurring topics across this work include AI in cancer detection (9 papers), Prostate Cancer Diagnosis and Treatment (5 papers), Image and Signal Denoising Methods (4 papers), Ultrasound Imaging and Elastography (4 papers), Advanced Steganography and Watermarking Techniques (4 papers), Advanced Image Fusion Techniques (3 papers), Domain Adaptation and Few-Shot Learning (3 papers) and Radiomics and Machine Learning in Medical Imaging (2 papers). The work is most often cited by research in Health Informatics (64 citations), Computer Vision and Pattern Recognition (362 citations) and Radiology, Nuclear Medicine and Imaging (333 citations). Shekoofeh Azizi has collaborated with scholars based in United States, Canada and South Korea. Frequent co-authors include Qiang Tang, Zili Yi, Daesik Jang, Zhan Xu, Mohammad Norouzi, Simon Kornblith, Alan Karthikesalingam, Fiona Ryan, Jan Freyberg and Ting Chen. Their work appears in journals such as Nature Medicine, SHILAP Revista de lepidopterología and Nature reviews. Cancer.
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.