Mateusz Buda
Impact in
- Health Informatics top 0.5%
- 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
-
- AI in cancer detection 8
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- Radiomics and Machine Learning in Medical Imaging 8
- Radiology practices and education 2
- COVID-19 diagnosis using AI 1
- Co-authors
- Maciej A. Mazurowski (13 shared papers)Atsuto Maki (1 shared paper)Ashirbani Saha (5 shared papers)Mustafa R. Bashir (1 shared paper)Benjamin Wildman‐Tobriner (4 shared papers)Jenny K. Hoang (3 shared papers)David Thayer (2 shared papers)William D. Middleton (2 shared papers)
- Journals
- Radiology (2 papers)Radiology Artificial Intelligence (1 paper)Scientific Reports (1 paper)Computers in Biology and Medicine (1 paper)JAMA Network Open (1 paper)
- Partner nations
- United StatesSweden
In The Last Decade
Mateusz Buda
13 papers receiving 2.6k citations
Hit Papers
Peers
Comparison fields: 5 of 171
- Health Informatics 180
- Artificial Intelligence 1.3k
- Radiology, Nuclear Medicine and Imaging 816
- Computer Vision and Pattern Recognition 624
- Neurology 182
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 | 1716 |
| 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 | 362 |
| 3 | 2019 | 199 | |
| 4 | 2019 | 152 | |
| 5 | 2019 | 103 | |
| 6 | 2021 | 63 | |
| 7 | 2020 | 33 | |
| 8 | 2019 | 31 | |
| 9 | 2020 | 16 | |
| 10 | 2021 | 14 | |
| 11 | 2023 | 4 | |
| 12 | 2020 | 3 | |
| 13 | 2020 | 1 |
About Mateusz Buda
Mateusz Buda is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Endocrinology, Diabetes and Metabolism, Computer Vision and Pattern Recognition and Pulmonary and Respiratory Medicine, having authored 13 papers that have together received 2.7k indexed citations. Recurring topics across this work include AI in cancer detection (8 papers), Radiomics and Machine Learning in Medical Imaging (8 papers), Thyroid Cancer Diagnosis and Treatment (4 papers), Digital Radiography and Breast Imaging (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Radiology practices and education (2 papers), COVID-19 diagnosis using AI (1 paper) and Electricity Theft Detection Techniques (1 paper). The work is most often cited by research in Health Informatics (180 citations), Artificial Intelligence (1.3k citations), Radiology, Nuclear Medicine and Imaging (816 citations), Computer Vision and Pattern Recognition (624 citations) and Neurology (182 citations). Mateusz Buda has collaborated with scholars based in United States and Sweden. Frequent co-authors include Maciej A. Mazurowski, Atsuto Maki, Ashirbani Saha, Mustafa R. Bashir, Benjamin Wildman‐Tobriner, Jenny K. Hoang, David Thayer, William D. Middleton, Franklin N. Tessler and Ryan G. Short. Their work appears in journals such as Radiology, Radiology Artificial Intelligence, Scientific Reports, Computers in Biology and Medicine and JAMA Network Open.
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.