Márta Fidrich
- Computer Vision and Pattern Recognition top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Biomedical Engineering
- Artificial Intelligence
- Archeology top 5%
- Co-authors
- László RuskóGábor NémethElizabeth BerryMichael A. SmithRichard BaldockZoltán GinglLászló RudasGérald Quatrehomme
- Topics
- Medical Image Segmentation Techniques (7 papers)Medical Imaging Techniques and Applications (5 papers)Radiomics and Machine Learning in Medical Imaging (4 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceMedical PhysicsMedical Image Analysis
- Partner nations
- HungaryIndiaUnited Kingdom
In The Last Decade
Márta Fidrich
13 papers receiving 434 citations
Peers
Comparison fields: 5 of 75
- Computer Vision and Pattern Recognition 236
- Radiology, Nuclear Medicine and Imaging 148
- Biomedical Engineering 107
- Artificial Intelligence 73
- Archeology 58
Countries citing papers authored by Márta Fidrich
This map shows the geographic impact of Márta Fidrich'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 Márta Fidrich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Márta Fidrich more than expected).
Fields of papers citing papers by Márta Fidrich
This network shows the impact of papers produced by Márta Fidrich. 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 Márta Fidrich. The network helps show where Márta Fidrich may publish in the future.
Co-authorship network of co-authors of Márta Fidrich
This figure shows the co-authorship network connecting the top 25 collaborators of Márta Fidrich. A scholar is included among the top collaborators of Márta Fidrich based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Márta Fidrich. Márta Fidrich is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 53 | |
| 2 | 5 | |
| 3 | 0 | |
| 4 | 70 | |
| 5 | 3 | |
| 6 | 79 | |
| 7 | 28 | |
| 8 | Fully automatic liver segmentation for contrast-enhanced CT images | 66 |
| 9 | 2 | |
| 10 | Development of a communication environment between IPv6 and IPv4 | 0 |
| 11 | 52 | |
| 12 | 6 | |
| 13 | 5 | |
| 14 | 85 | |
| 15 | Multiscale Extraction and Representation of Features from Medical Images | 3 |
About Márta Fidrich
Márta Fidrich is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Radiology, Nuclear Medicine and Imaging, having authored 15 papers that have together received 457 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (7 papers), Medical Imaging Techniques and Applications (5 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (236 citations), Radiology, Nuclear Medicine and Imaging (148 citations) and Archeology (58 citations). Márta Fidrich has collaborated with scholars based in Hungary, India and United Kingdom. Frequent co-authors include László Ruskó, Gábor Németh, Elizabeth Berry, Michael A. Smith, Richard Baldock, Zoltán Gingl, László Rudas, Gérald Quatrehomme, Stéphane Cotin and Paul Bailet. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Medical Physics 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.