Ulaş Bağcı
Impact in
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
- Medical Imaging Techniques and Applications
- Health Informatics top 1%
Papers in
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- Radiomics and Machine Learning in Medical Imaging 68
- COVID-19 diagnosis using AI 26
- Medical Imaging Techniques and Applications 25
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- Medical Image Segmentation Techniques 47
- Advanced Neural Network Applications 29
- Co-authors
- Daniel J. Mollura (39 shared papers)Ziyue Xu (26 shared papers)Jayaram K. Udupa (16 shared papers)Brent Foster (13 shared papers)Xinjian Chen (15 shared papers)Awais Mansoor (9 shared papers)Sarfaraz Hussein (8 shared papers)Jianhua Yao (15 shared papers)
- Journals
- IEEE Transactions on Medical Imaging (7 papers)Lecture notes in computer science (37 papers)IEEE Transactions on Biomedical Engineering (4 papers)Gastroenterology (4 papers)Medical Image Analysis (4 papers)
- Partner nations
- United StatesTürkiyeUnited Kingdom
In The Last Decade
Ulaş Bağcı
234 papers receiving 5.2k citations
Ulaş Bağcı's Hit Papers
Peers
Comparison fields: 5 of 167
- Radiology, Nuclear Medicine and Imaging 2.3k
- Health Informatics 139
- Computer Vision and Pattern Recognition 1.4k
- Neurology 306
- Artificial Intelligence 1.1k
Countries citing papers authored by Ulaş Bağcı
This map shows the geographic impact of Ulaş Bağcı'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 Ulaş Bağcı with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ulaş Bağcı more than expected).
Fields of papers citing papers by Ulaş Bağcı
This network shows the impact of papers produced by Ulaş Bağcı. 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 Ulaş Bağcı. The network helps show where Ulaş Bağcı may publish in the future.
Co-authors
The 25 scholars most cited alongside Ulaş Bağcı, 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 258 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2014 | 280 | |
| 2 | 2019 | 216 | |
| 3 | 2016 | 200 | |
| 4 | 2015 | 192 | |
| 5 | 2012 | 179 | |
| 6 | 2023 | 152 | |
| 7 | 2018 | 137 | |
| 8 | 2013 | 136 | |
| 9 | Real-time Multi-Class Helmet Violation Detection Using Few-Shot Data Sampling Technique and YOLOv8 Hit paper breakdown → | 2023 | 135 |
| 10 | 2014 | 133 | |
| 11 | Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation Hit paper breakdown → | 2024 | 123 |
| 12 | 2022 | 119 | |
| 13 | 2017 | 118 | |
| 14 | 2018 | 107 | |
| 15 | 2019 | 100 | |
| 16 | 2020 | 98 | |
| 17 | 2018 | 93 | |
| 18 | 2017 | 70 | |
| 19 | 2021 | 66 | |
| 20 | 2011 | 63 |
About Ulaş Bağcı
Ulaş Bağcı is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Artificial Intelligence, Biomedical Engineering and Pulmonary and Respiratory Medicine, having authored 258 papers that have together received 5.3k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (68 papers), Medical Image Segmentation Techniques (47 papers), AI in cancer detection (43 papers), Advanced Neural Network Applications (29 papers), COVID-19 diagnosis using AI (26 papers), Medical Imaging Techniques and Applications (25 papers), Medical Imaging and Analysis (23 papers) and Brain Tumor Detection and Classification (16 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (2.3k citations), Health Informatics (139 citations), Computer Vision and Pattern Recognition (1.4k citations), Neurology (306 citations) and Artificial Intelligence (1.1k citations). Ulaş Bağcı has collaborated with scholars based in United States, Türkiye and United Kingdom. Frequent co-authors include Daniel J. Mollura, Ziyue Xu, Jayaram K. Udupa, Brent Foster, Xinjian Chen, Awais Mansoor, Sarfaraz Hussein, Jianhua Yao, Debesh Jha and Georgios Z. Papadakis. Their work appears in journals such as IEEE Transactions on Medical Imaging, Lecture notes in computer science, IEEE Transactions on Biomedical Engineering, Gastroenterology and Medical Image Analysis.
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.