Michael Mavroforakis
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Molecular Biology
- Computational Mechanics top 10%
- Co-authors
- Sergios TheodoridisHarris GeorgiouNikos DimitropoulosD. CavourasPantelis BouboulisDionisis Cavouras
- Topics
- AI in cancer detection (8 papers)Radiomics and Machine Learning in Medical Imaging (3 papers)Advanced Optimization Algorithms Research (3 papers)
- Cited by
- Computer Vision and Pattern RecognitionArtificial IntelligenceRadiology, Nuclear Medicine and Imaging
- Journals
- IEEE Transactions on Signal ProcessingIEEE Signal Processing MagazineComputer Methods and Programs in Biomedicine
- Partner nations
- GreeceUnited States
In The Last Decade
Michael Mavroforakis
15 papers receiving 639 citations
Peers
Comparison fields: 5 of 115
- Artificial Intelligence 366
- Computer Vision and Pattern Recognition 306
- Radiology, Nuclear Medicine and Imaging 124
- Molecular Biology 77
- Computational Mechanics 70
Countries citing papers authored by Michael Mavroforakis
This map shows the geographic impact of Michael Mavroforakis'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 Michael Mavroforakis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Mavroforakis more than expected).
Fields of papers citing papers by Michael Mavroforakis
This network shows the impact of papers produced by Michael Mavroforakis. 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 Michael Mavroforakis. The network helps show where Michael Mavroforakis may publish in the future.
Co-authorship network of co-authors of Michael Mavroforakis
This figure shows the co-authorship network connecting the top 25 collaborators of Michael Mavroforakis. A scholar is included among the top collaborators of Michael Mavroforakis 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 Michael Mavroforakis. Michael Mavroforakis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 33 | |
| 2 | 22 | |
| 3 | 94 | |
| 4 | 1 | |
| 5 | 9 | |
| 6 | 37 | |
| 7 | Reduced Convex Hulls: A Geometric Approach to Support Vector Machines | 13 |
| 8 | 20 | |
| 9 | 88 | |
| 10 | 289 | |
| 11 | 16 | |
| 12 | 28 | |
| 13 | 13 | |
| 14 | 10 | |
| 15 | 1 |
About Michael Mavroforakis
Michael Mavroforakis is a scholar working on Numerical Analysis, Computer Vision and Pattern Recognition and Computer Graphics and Computer-Aided Design, having authored 15 papers that have together received 674 indexed citations. Recurring topics across this work include AI in cancer detection (8 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Advanced Optimization Algorithms Research (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (306 citations), Artificial Intelligence (366 citations) and Radiology, Nuclear Medicine and Imaging (124 citations). Michael Mavroforakis has collaborated with scholars based in Greece and United States. Frequent co-authors include Sergios Theodoridis, Harris Georgiou, Nikos Dimitropoulos, D. Cavouras, Pantelis Bouboulis and Dionisis Cavouras. Their work appears in journals such as IEEE Transactions on Signal Processing, IEEE Signal Processing Magazine and Computer Methods and Programs in Biomedicine.
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.