Samreen Anjum
Impact in
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- Cancer Immunotherapy and Biomarkers
- CAR-T cell therapy research
- Cancer Cells and Metastasis
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- Immunotherapy and Immune Responses
- Immune cells in cancer
Papers in
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- Multimodal Machine Learning Applications 3
- Human Pose and Action Recognition 2
- Advanced Neural Network Applications 1
- Image and Video Quality Assessment 1
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- Domain Adaptation and Few-Shot Learning 4
- Co-authors
- Danna Gurari (7 shared papers)Chongyan Chen (2 shared papers)Wouter Hendrickx (1 shared paper)Ena Wang (1 shared paper)Sara Tomei (1 shared paper)Ines Simeone (1 shared paper)Barbara Seliger (1 shared paper)François Bertucci (1 shared paper)
- Journals
- OncoImmunology (1 paper)BMC Bioinformatics (1 paper)Proceedings of the ACM on Human-Computer Interaction (1 paper)Proceedings of the Association for Information Science and Technology (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)
- Partner nations
- United StatesUnited KingdomItaly
In The Last Decade
Samreen Anjum
9 papers receiving 197 citations
Peers
Comparison fields: 5 of 58
- Oncology 101
- Immunology 56
- Computer Vision and Pattern Recognition 47
- Cancer Research 30
- Biophysics 10
Countries citing papers authored by Samreen Anjum
This map shows the geographic impact of Samreen Anjum'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 Samreen Anjum with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Samreen Anjum more than expected).
Fields of papers citing papers by Samreen Anjum
This network shows the impact of papers produced by Samreen Anjum. 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 Samreen Anjum. The network helps show where Samreen Anjum may publish in the future.
Co-authors
The 22 scholars most cited alongside Samreen Anjum, 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 | 2017 | 128 | |
| 2 | 2022 | 29 | |
| 3 | 2020 | 18 | |
| 4 | 2019 | 7 | |
| 5 | 2021 | 6 | |
| 6 | 2021 | 4 | |
| 7 | 2022 | 4 | |
| 8 | 2015 | 3 | |
| 9 | 2023 | 3 |
About Samreen Anjum
Samreen Anjum is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Science Applications, Cancer Research and Molecular Biology, having authored 9 papers that have together received 202 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (4 papers), Multimodal Machine Learning Applications (3 papers), Cancer Genomics and Diagnostics (2 papers), Human Pose and Action Recognition (2 papers), Mobile Crowdsensing and Crowdsourcing (2 papers), Advanced Neural Network Applications (1 paper), Image and Video Quality Assessment (1 paper) and COVID-19 diagnosis using AI (1 paper). The work is most often cited by research in Oncology (101 citations), Immunology (56 citations), Computer Vision and Pattern Recognition (47 citations), Cancer Research (30 citations) and Biophysics (10 citations). Samreen Anjum has collaborated with scholars based in United States, United Kingdom and Italy. Frequent co-authors include Danna Gurari, Chongyan Chen, Wouter Hendrickx, Ena Wang, Sara Tomei, Ines Simeone, Barbara Seliger, François Bertucci, Francesco M. Marincola and Luigi Cerulo. Their work appears in journals such as OncoImmunology, BMC Bioinformatics, Proceedings of the ACM on Human-Computer Interaction, Proceedings of the Association for Information Science and Technology and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.