Bassem Ben Cheikh
Impact in
-
- Advanced Neural Network Applications
- Medical Image Segmentation Techniques
- Digital Imaging for Blood Diseases
- Biophysics top 5%
- Cell Image Analysis Techniques
Papers in
- Oncology 7
- Colorectal Cancer Screening and Detection 3
-
- AI in cancer detection 6
- Co-authors
- Daniel Racoceanu (6 shared papers)Martin Urschler (1 shared paper)Bogdan J. Matuszewski (1 shared paper)Olaf Ronneberger (1 shared paper)Michael Pfeiffer (1 shared paper)Hao Chen (1 shared paper)Pheng‐Ann Heng (1 shared paper)David Snead (1 shared paper)
- Journals
- Cancer Research (3 papers)The Journal of Immunology (1 paper)Nature Communications (1 paper)Frontiers in Immunology (1 paper)Pattern Recognition Letters (1 paper)
- Partner nations
- FranceAustraliaUnited States
In The Last Decade
Bassem Ben Cheikh
17 papers receiving 768 citations
Bassem Ben Cheikh's Hit Papers
Peers
Comparison fields: 5 of 70
- Computer Vision and Pattern Recognition 344
- Biophysics 80
- Artificial Intelligence 443
- Radiology, Nuclear Medicine and Imaging 299
- Health Informatics 13
Countries citing papers authored by Bassem Ben Cheikh
This map shows the geographic impact of Bassem Ben Cheikh'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 Bassem Ben Cheikh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bassem Ben Cheikh more than expected).
Fields of papers citing papers by Bassem Ben Cheikh
This network shows the impact of papers produced by Bassem Ben Cheikh. 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 Bassem Ben Cheikh. The network helps show where Bassem Ben Cheikh may publish in the future.
Co-authors
The 25 scholars most cited alongside Bassem Ben Cheikh, 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 | Gland segmentation in colon histology images: The glas challenge contest Hit paper breakdown → | 2016 | 570 |
| 2 | 2021 | 103 | |
| 3 | 2021 | 42 | |
| 4 | 2023 | 20 | |
| 5 | 2016 | 9 | |
| 6 | 2024 | 8 | |
| 7 | 2022 | 5 | |
| 8 | 2017 | 5 | |
| 9 | 2016 | 4 | |
| 10 | 2014 | 3 | |
| 11 | 2017 | 3 | |
| 12 | 2023 | 1 | |
| 13 | 2023 | 1 | |
| 14 | 2021 | 1 | |
| 15 | 2015 | 1 | |
| 16 | 2017 | 1 | |
| 17 | Preliminary approach for crypt detection in Inflammatory Bowel Disease | 2015 | 1 |
| 18 | 2024 | 0 |
About Bassem Ben Cheikh
Bassem Ben Cheikh is a scholar working on Oncology, Artificial Intelligence, Molecular Biology, Structural Biology and Radiation, having authored 18 papers that have together received 778 indexed citations. Recurring topics across this work include AI in cancer detection (6 papers), Single-cell and spatial transcriptomics (3 papers), Cell Image Analysis Techniques (3 papers), Electron and X-Ray Spectroscopy Techniques (3 papers), Advanced X-ray Imaging Techniques (3 papers), Colorectal Cancer Screening and Detection (3 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Advanced Electron Microscopy Techniques and Applications (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (344 citations), Biophysics (80 citations), Artificial Intelligence (443 citations), Radiology, Nuclear Medicine and Imaging (299 citations) and Health Informatics (13 citations). Bassem Ben Cheikh has collaborated with scholars based in France, Australia and United States. Frequent co-authors include Daniel Racoceanu, Martin Urschler, Bogdan J. Matuszewski, Olaf Ronneberger, Michael Pfeiffer, Hao Chen, Pheng‐Ann Heng, David Snead, Elia Bruni and Korsuk Sirinukunwattana. Their work appears in journals such as Cancer Research, The Journal of Immunology, Nature Communications, Frontiers in Immunology and Pattern Recognition Letters.
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.