Eric Arazo
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- Advanced Neural Network Applications 6
- Medical Image Segmentation Techniques 2
- Artificial Intelligence top 5%
- Machine Learning and Data Classification 5
- Domain Adaptation and Few-Shot Learning 4
- Imbalanced Data Classification Techniques 1
- Media Technology top 10%
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- Radiomics and Machine Learning in Medical Imaging 2
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- Medical Imaging and Analysis 1
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- Colorectal Cancer Screening and Detection 1
- Journals
- Expert Systems (1 paper)Dublin City University Open Access Institutional Repository (Dublin City University) (1 paper)2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (1 paper)
In The Last Decade
Eric Arazo
9 papers receiving 546 citations
Hit Papers
Peers
Comparison fields: 5 of 80
- Computer Vision and Pattern Recognition 264
- Artificial Intelligence 380
- Media Technology 31
- Radiology, Nuclear Medicine and Imaging 59
- Signal Processing 27
Countries citing papers authored by Eric Arazo
This map shows the geographic impact of Eric Arazo'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 Eric Arazo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eric Arazo more than expected).
Fields of papers citing papers by Eric Arazo
This network shows the impact of papers produced by Eric Arazo. 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 Eric Arazo. The network helps show where Eric Arazo may publish in the future.
Co-authorship network
The 7 scholars most cited alongside Eric Arazo, 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 | 2024 | 3 | |
| 2 | 2024 | 15 | |
| 3 | 2023 | 0 | |
| 4 | 2023 | 8 | |
| 5 | 2022 | 12 | |
| 6 | 2022 | 2 | |
| 7 | 2021 | 5 | |
| 8 | Pseudo-labeling and confirmation bias in deep semi-supervised learningbreakdown → | 2020 | 418 |
| 9 | 2019 | 104 | |
| 10 | 2019 | 1 |
About Eric Arazo
Eric Arazo is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Signal Processing and Oncology, having authored 10 papers that have together received 568 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (6 papers), Machine Learning and Data Classification (5 papers), Domain Adaptation and Few-Shot Learning (4 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Medical Image Segmentation Techniques (2 papers), Medical Imaging and Analysis (1 paper), Imbalanced Data Classification Techniques (1 paper) and Colorectal Cancer Screening and Detection (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (264 citations), Artificial Intelligence (380 citations), Media Technology (31 citations), Radiology, Nuclear Medicine and Imaging (59 citations) and Signal Processing (27 citations). Eric Arazo has collaborated with scholars based in Ireland and Spain. Frequent co-authors include Noel E. O’Connor, Kevin McGuinness, Paul Albert, Diego Ortego, Suzanne Little, Kathleen M. Curran and Noel E. O’Connor. Their work appears in journals such as Expert Systems, Dublin City University Open Access Institutional Repository (Dublin City University), 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) and arXiv (Cornell University).
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.