Máté E. Maros
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
-
- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in ⓘ
- Neurology 11
- Traumatic Brain Injury and Neurovascular Disturbances 7
- Intracranial Aneurysms: Treatment and Complications 4
- Epidemiology 11
- Acute Ischemic Stroke Management 8
- Co-authors
- Thomas Ganslandt (8 shared papers)Kim Eun Hee (5 shared papers)Nandhini Santhanam (2 shared papers)Alejandro Cosa‐Linan (2 shared papers)Mahboubeh Jannesari (2 shared papers)Tibor Krenács (8 shared papers)Péter Balla (6 shared papers)Gergő Kiszner (3 shared papers)
In The Last Decade
Máté E. Maros
40 papers receiving 893 citations
Hit Papers
Peers
Comparison fields: 5 of 122
- Health Informatics 51
- Radiology, Nuclear Medicine and Imaging 266
- Artificial Intelligence 195
- Neurology 50
- Biophysics 32
Countries citing papers authored by Máté E. Maros
This map shows the geographic impact of Máté E. Maros'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 Máté E. Maros with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Máté E. Maros more than expected).
Fields of papers citing papers by Máté E. Maros
This network shows the impact of papers produced by Máté E. Maros. 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 Máté E. Maros. The network helps show where Máté E. Maros may publish in the future.
Co-authors
The 25 scholars most cited alongside Máté E. Maros, 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 43 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Transfer learning for medical image classification: a literature review Hit paper breakdown → | 2022 | 442 |
| 2 | 2020 | 84 | |
| 3 | 2014 | 76 | |
| 4 | 2014 | 69 | |
| 5 | 2017 | 19 | |
| 6 | 2015 | 17 | |
| 7 | 2015 | 17 | |
| 8 | 2021 | 15 | |
| 9 | 2014 | 13 | |
| 10 | 2019 | 12 | |
| 11 | 2019 | 11 | |
| 12 | 2018 | 11 | |
| 13 | 2021 | 9 | |
| 14 | 2019 | 8 | |
| 15 | 2015 | 8 | |
| 16 | 2024 | 7 | |
| 17 | 2016 | 7 | |
| 18 | 2018 | 7 | |
| 19 | 2016 | 7 | |
| 20 | 2021 | 6 |
About Máté E. Maros
Máté E. Maros is a scholar working on Neurology, Epidemiology, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging and Molecular Biology, having authored 43 papers that have together received 906 indexed citations. Recurring topics across this work include Acute Ischemic Stroke Management (8 papers), Traumatic Brain Injury and Neurovascular Disturbances (7 papers), Stroke Rehabilitation and Recovery (5 papers), Intracranial Aneurysms: Treatment and Complications (4 papers), Cerebrovascular and Carotid Artery Diseases (4 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), COVID-19 diagnosis using AI (3 papers) and Venous Thromboembolism Diagnosis and Management (3 papers). The work is most often cited by research in Health Informatics (51 citations), Radiology, Nuclear Medicine and Imaging (266 citations), Artificial Intelligence (195 citations), Neurology (50 citations) and Biophysics (32 citations). Máté E. Maros has collaborated with scholars based in Germany, Hungary and Italy. Frequent co-authors include Thomas Ganslandt, Kim Eun Hee, Nandhini Santhanam, Alejandro Cosa‐Linan, Mahboubeh Jannesari, Tibor Krenács, Péter Balla, Gergő Kiszner, Nóra Meggyesházi and Ivett Teleki. Their work appears in journals such as PLoS ONE, Clinical Neuroradiology, Biomedicines, Pathology & Oncology Research and Frontiers in Neurology.
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.