Mateusz Buda
Impact in
- Health Informatics top 1%
- Artificial Intelligence top 1%
- AI in cancer detection
- Imbalanced Data Classification Techniques
- Anomaly Detection Techniques and Applications
- Domain Adaptation and Few-Shot Learning
Papers in
-
- Radiomics and Machine Learning in Medical Imaging 5
- Radiology practices and education 2
- COVID-19 diagnosis using AI 1
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- AI in cancer detection 7
- Co-authors
- Maciej A. Mazurowski (13 shared papers)Atsuto Maki (1 shared paper)Ashirbani Saha (5 shared papers)Mustafa Rifaat Bashir (1 shared paper)Benjamin Wildman‐Tobriner (4 shared papers)Jenny K. Hoang (3 shared papers)David Thayer (2 shared papers)William Dana Middleton (2 shared papers)
- Journals
- JAMA Network Open (2 papers)Radiology (2 papers)Journal of Magnetic Resonance Imaging (1 paper)Ultrasound in Medicine & Biology (1 paper)Scientific Reports (1 paper)
- Partner nations
- United StatesPolandIsrael
In The Last Decade
Mateusz Buda
14 papers receiving 3.1k citations
Mateusz Buda's Hit Papers
Peers
Comparison fields: 5 of 173
- Health Informatics 161
- Artificial Intelligence 1.4k
- Radiology, Nuclear Medicine and Imaging 747
- Computer Vision and Pattern Recognition 729
- Neurology 195
Countries citing papers authored by Mateusz Buda
This map shows the geographic impact of Mateusz Buda'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 Mateusz Buda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mateusz Buda more than expected).
Fields of papers citing papers by Mateusz Buda
This network shows the impact of papers produced by Mateusz Buda. 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 Mateusz Buda. The network helps show where Mateusz Buda may publish in the future.
Co-authors
The 24 scholars most cited alongside Mateusz Buda, 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 | A systematic study of the class imbalance problem in convolutional neural networks Hit paper breakdown → | 2018 | 2052 |
| 2 | Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI Hit paper breakdown → | 2018 | 414 |
| 3 | 2019 | 244 | |
| 4 | 2019 | 168 | |
| 5 | 2019 | 117 | |
| 6 | 2021 | 77 | |
| 7 | 2020 | 39 | |
| 8 | 2019 | 33 | |
| 9 | 2023 | 17 | |
| 10 | 2020 | 17 | |
| 11 | 2021 | 16 | |
| 12 | 2023 | 7 | |
| 13 | 2020 | 3 | |
| 14 | 2020 | 1 |
About Mateusz Buda
Mateusz Buda is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Endocrinology, Diabetes and Metabolism, Computer Vision and Pattern Recognition and Biophysics, having authored 14 papers that have together received 3.2k indexed citations. Recurring topics across this work include AI in cancer detection (7 papers), Radiomics and Machine Learning in Medical Imaging (5 papers), Thyroid Cancer Diagnosis and Treatment (4 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Digital Radiography and Breast Imaging (2 papers), Radiology practices and education (2 papers), Total Knee Arthroplasty Outcomes (1 paper) and COVID-19 diagnosis using AI (1 paper). The work is most often cited by research in Health Informatics (161 citations), Artificial Intelligence (1.4k citations), Radiology, Nuclear Medicine and Imaging (747 citations), Computer Vision and Pattern Recognition (729 citations) and Neurology (195 citations). Mateusz Buda has collaborated with scholars based in United States, Poland and Israel. Frequent co-authors include Maciej A. Mazurowski, Atsuto Maki, Ashirbani Saha, Mustafa Rifaat Bashir, Benjamin Wildman‐Tobriner, Jenny K. Hoang, David Thayer, William Dana Middleton, Franklin Neil Tessler and Ryan G. Short. Their work appears in journals such as JAMA Network Open, Radiology, Journal of Magnetic Resonance Imaging, Ultrasound in Medicine & Biology and Scientific Reports.
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.