Akis Linardos
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in
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- AI in cancer detection 4
- Privacy-Preserving Technologies in Data 1
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- Artificial Intelligence in Healthcare and Education 4
- Co-authors
- Kaisar Kushibar (5 shared papers)Karim Lekadir (5 shared papers)Polyxeni Gkontra (2 shared papers)Richard Osuala (4 shared papers)Seán Walsh (1 shared paper)Lidia Garrucho (4 shared papers)Oliver Díaz (4 shared papers)Matthias Kümmerer (1 shared paper)
- Journals
- Scientific Reports (1 paper)Medical Image Analysis (1 paper)Physica Medica (1 paper)arXiv (Cornell University) (1 paper)2021 IEEE/CVF International Conference on Computer Vision (ICCV) (1 paper)
- Partner nations
- SpainGermanyNetherlands
In The Last Decade
Akis Linardos
6 papers receiving 245 citations
Peers
Comparison fields: 5 of 58
- Health Informatics 51
- Radiology, Nuclear Medicine and Imaging 97
- Artificial Intelligence 112
- Computer Vision and Pattern Recognition 67
- Human-Computer Interaction 16
Countries citing papers authored by Akis Linardos
This map shows the geographic impact of Akis Linardos'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 Akis Linardos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Akis Linardos more than expected).
Fields of papers citing papers by Akis Linardos
This network shows the impact of papers produced by Akis Linardos. 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 Akis Linardos. The network helps show where Akis Linardos may publish in the future.
Co-authors
The 14 scholars most cited alongside Akis Linardos, 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 | 2021 | 84 | |
| 2 | 2022 | 72 | |
| 3 | 2021 | 44 | |
| 4 | 2022 | 43 | |
| 5 | A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions. | 2021 | 9 |
| 6 | 2021 | 1 |
About Akis Linardos
Akis Linardos is a scholar working on Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging, Cognitive Neuroscience and Computer Vision and Pattern Recognition, having authored 6 papers that have together received 253 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (4 papers), AI in cancer detection (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Privacy-Preserving Technologies in Data (1 paper), Advanced Neuroimaging Techniques and Applications (1 paper), Functional Brain Connectivity Studies (1 paper), Visual Attention and Saliency Detection (1 paper) and Olfactory and Sensory Function Studies (1 paper). The work is most often cited by research in Health Informatics (51 citations), Radiology, Nuclear Medicine and Imaging (97 citations), Artificial Intelligence (112 citations), Computer Vision and Pattern Recognition (67 citations) and Human-Computer Interaction (16 citations). Akis Linardos has collaborated with scholars based in Spain, Germany and Netherlands. Frequent co-authors include Kaisar Kushibar, Karim Lekadir, Polyxeni Gkontra, Richard Osuala, Seán Walsh, Lidia Garrucho, Oliver Díaz, Matthias Kümmerer, Matthias Bethge and Fred Prior. Their work appears in journals such as Scientific Reports, Medical Image Analysis, Physica Medica, arXiv (Cornell University) and 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
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.