Giulia Vatteroni
Impact in
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- MRI in cancer diagnosis
- Radiomics and Machine Learning in Medical Imaging
- Medical Imaging Techniques and Applications
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- Digital Radiography and Breast Imaging
- Prostate Cancer Diagnosis and Treatment
Papers in
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- MRI in cancer diagnosis 8
- Radiomics and Machine Learning in Medical Imaging 6
- Medical Imaging Techniques and Applications 3
- Radiation Dose and Imaging 3
- COVID-19 diagnosis using AI 2
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- Digital Radiography and Breast Imaging 3
- Co-authors
- Paola Clauser (4 shared papers)Pascal Baltzer (6 shared papers)Thomas H. Helbich (4 shared papers)Nina Pötsch (3 shared papers)Shahrokh F. Shariat (1 shared paper)Luca Balzarini (4 shared papers)Stephan Korn (1 shared paper)Elena Casiraghi (2 shared papers)
In The Last Decade
Giulia Vatteroni
12 papers receiving 248 citations
Peers
Comparison fields: 5 of 41
- Radiology, Nuclear Medicine and Imaging 150
- Pulmonary and Respiratory Medicine 116
- Cancer Research 48
- Health Informatics 4
- Pathology and Forensic Medicine 34
Countries citing papers authored by Giulia Vatteroni
This map shows the geographic impact of Giulia Vatteroni'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 Giulia Vatteroni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Giulia Vatteroni more than expected).
Fields of papers citing papers by Giulia Vatteroni
This network shows the impact of papers produced by Giulia Vatteroni. 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 Giulia Vatteroni. The network helps show where Giulia Vatteroni may publish in the future.
Co-authors
The 25 scholars most cited alongside Giulia Vatteroni, 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 | 2022 | 90 | |
| 2 | 2022 | 35 | |
| 3 | 2020 | 30 | |
| 4 | 2023 | 24 | |
| 5 | 2022 | 16 | |
| 6 | 2021 | 15 | |
| 7 | 2022 | 14 | |
| 8 | 2024 | 9 | |
| 9 | 2022 | 9 | |
| 10 | 2023 | 5 | |
| 11 | 2021 | 2 | |
| 12 | 2024 | 1 | |
| 13 | 2017 | 1 | |
| 14 | 2025 | 0 | |
| 15 | 2024 | 0 | |
| 16 | 2025 | 0 | |
| 17 | 2025 | 0 | |
| 18 | 2026 | 0 | |
| 19 | 2024 | 0 | |
| 20 | 2024 | 0 |
About Giulia Vatteroni
Giulia Vatteroni is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Pathology and Forensic Medicine, Cancer Research and Infectious Diseases, having authored 20 papers that have together received 251 indexed citations. Recurring topics across this work include MRI in cancer diagnosis (8 papers), Radiomics and Machine Learning in Medical Imaging (6 papers), Breast Cancer Treatment Studies (4 papers), Breast Lesions and Carcinomas (4 papers), Medical Imaging Techniques and Applications (3 papers), Digital Radiography and Breast Imaging (3 papers), Radiation Dose and Imaging (3 papers) and COVID-19 diagnosis using AI (2 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (150 citations), Pulmonary and Respiratory Medicine (116 citations), Cancer Research (48 citations), Health Informatics (4 citations) and Pathology and Forensic Medicine (34 citations). Giulia Vatteroni has collaborated with scholars based in Italy, Austria and Germany. Frequent co-authors include Paola Clauser, Pascal Baltzer, Thomas H. Helbich, Nina Pötsch, Shahrokh F. Shariat, Luca Balzarini, Stephan Korn, Elena Casiraghi, Caterina Giannitto and Giorgio Maria Ferraroli. Their work appears in journals such as European Journal of Radiology, European Radiology, La radiologia medica, Expert Opinion on Drug Safety and Investigative Radiology.
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.