Matej Gazda
Impact in
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- Voice and Speech Disorders
Papers in
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- Medical Image Segmentation Techniques 3
- Generative Adversarial Networks and Image Synthesis 2
- Advanced Neural Network Applications 2
- Co-authors
- Peter DrotárLiberios VokorokosDinesh KumarNemuel Daniel PahJakub GazdaMohammod Abdul MotinJán PlavkaL. J. Peters
- Journals
- Information Sciences (1 paper)International Journal of Radiation Oncology*Biology*Physics (1 paper)Journal of Personalized Medicine (1 paper)Computer Methods and Programs in Biomedicine (1 paper)IEEE Access (1 paper)
- Partner nations
- SlovakiaUnited StatesCanada
In The Last Decade
Matej Gazda
15 papers receiving 341 citations
Peers
Comparison fields: 5 of 74
- Health Informatics 7
- Physiology 113
- Neurology 61
- Signal Processing 42
- Artificial Intelligence 125
Countries citing papers authored by Matej Gazda
This map shows the geographic impact of Matej Gazda'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 Matej Gazda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matej Gazda more than expected).
Fields of papers citing papers by Matej Gazda
This network shows the impact of papers produced by Matej Gazda. 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 Matej Gazda. The network helps show where Matej Gazda may publish in the future.
Co-authors
The 25 scholars most cited alongside Matej Gazda, 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 | 2025 | 0 | |
| 2 | 2023 | 1 | |
| 3 | 2022 | 10 | |
| 4 | 2022 | 19 | |
| 5 | 2022 | 1 | |
| 6 | 2022 | 10 | |
| 7 | 2022 | 8 | |
| 8 | 2021 | 45 | |
| 9 | 2021 | 88 | |
| 10 | 2021 | 4 | |
| 11 | 2021 | 6 | |
| 12 | 2021 | 59 | |
| 13 | 2020 | 1 | |
| 14 | 2018 | 70 | |
| 15 | 2017 | 7 | |
| 16 | 1992 | 27 |
About Matej Gazda
Matej Gazda is a scholar working on Hepatology, Computer Vision and Pattern Recognition, Signal Processing, Neurology and Radiology, Nuclear Medicine and Imaging, having authored 16 papers that have together received 356 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (3 papers), Voice and Speech Disorders (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Liver Disease Diagnosis and Treatment (2 papers), Parkinson's Disease Mechanisms and Treatments (2 papers), Pediatric Hepatobiliary Diseases and Treatments (2 papers) and Advanced Neural Network Applications (2 papers). The work is most often cited by research in Health Informatics (7 citations), Physiology (113 citations), Neurology (61 citations), Signal Processing (42 citations) and Artificial Intelligence (125 citations). Matej Gazda has collaborated with scholars based in Slovakia, United States and Canada. Frequent co-authors include Peter Drotár, Liberios Vokorokos, Dinesh Kumar, Nemuel Daniel Pah, Jakub Gazda, Mohammod Abdul Motin, Ján Plavka, L. J. Peters, L. Clifton Stephens and Timothy E. Schultheiss. Their work appears in journals such as Information Sciences, International Journal of Radiation Oncology*Biology*Physics, Journal of Personalized Medicine, Computer Methods and Programs in Biomedicine and IEEE Access.
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.