Enzo Ferrante
- Health Informatics top 0.5%
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- Radiomics and Machine Learning in Medical Imaging 6
- Medical Imaging Techniques and Applications 5
- COVID-19 diagnosis using AI 5
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- Medical Image Segmentation Techniques 12
- Neurology top 5%
- Brain Tumor Detection and Classification 5
- Cognitive Neuroscience top 5%
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- AI in cancer detection 6
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- Medical Imaging and Analysis 5
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- Asthma and respiratory diseases 5
Enzo Ferrante
62 papers receiving 2.3k citations
Hit Papers
Peers
Comparison fields: 5 of 154
- Health Informatics 254
- Radiology, Nuclear Medicine and Imaging 812
- Computer Vision and Pattern Recognition 563
- Neurology 206
- Cognitive Neuroscience 477
Countries citing papers authored by Enzo Ferrante
This map shows the geographic impact of Enzo Ferrante'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 Enzo Ferrante with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Enzo Ferrante more than expected).
Fields of papers citing papers by Enzo Ferrante
This network shows the impact of papers produced by Enzo Ferrante. 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 Enzo Ferrante. The network helps show where Enzo Ferrante may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Enzo Ferrante, 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 | 2025 | 1 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 7 | |
| 4 | 2024 | 0 | |
| 5 | 2024 | 8 | |
| 6 | 2024 | 19 | |
| 7 | 2023 | 3 | |
| 8 | 2022 | 30 | |
| 9 | 2022 | 107 | |
| 10 | 2021 | 1 | |
| 11 | 2021 | 33 | |
| 12 | 2021 | 1 | |
| 13 | Inteligencia artificial y sesgos algorítmicos: ¿Por qué deberían importarnos? | 2021 | 1 |
| 14 | 2020 | 56 | |
| 15 | 2020 | 324 | |
| 16 | 2019 | 1 | |
| 17 | Segmentación multi-atlas de imágenes médicas con selección de atlas inteligente y control de calidad automático | 2018 | 1 |
| 18 | Spectral Graph Convolutions on Population Graphs for Disease Prediction | 2017 | 2 |
| 19 | Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentationbreakdown → | 2017 | 438 |
| 20 | 2015 | 10 |
About Enzo Ferrante
Enzo Ferrante is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Neurology and Artificial Intelligence, having authored 65 papers that have together received 2.3k indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (12 papers), Radiomics and Machine Learning in Medical Imaging (6 papers), AI in cancer detection (6 papers), Medical Imaging Techniques and Applications (5 papers), COVID-19 diagnosis using AI (5 papers), Brain Tumor Detection and Classification (5 papers), Medical Imaging and Analysis (5 papers) and Asthma and respiratory diseases (5 papers). The work is most often cited by research in Health Informatics (254 citations), Radiology, Nuclear Medicine and Imaging (812 citations), Computer Vision and Pattern Recognition (563 citations), Neurology (206 citations) and Cognitive Neuroscience (477 citations). Enzo Ferrante has collaborated with scholars based in Argentina, United Kingdom and Italy. Frequent co-authors include Daniel Rueckert, Ben Glocker, Sarah Parisot, Diego H. Milone, Sofia Ira Ktena, Matthew Lee, Nikos Paragios, Victoria Peterson, Nicolás Nieto and Agostina J. Larrazabal. Their work appears in journals such as Respiration, Medical Image Analysis, European Radiology, GigaScience and IEEE Transactions on Medical Imaging.
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.