Veronica Magni

510 citations
12 papers · 253 indexed · h-index 9
Topics
Digital Radiography and Breast Imaging (7 papers)Radiomics and Machine Learning in Medical Imaging (5 papers)Breast Lesions and Carcinomas (5 papers)

In The Last Decade

Veronica Magni

12 papers receiving 251 citations

Peers

Veronica Magni
Comparison fields: 5 of 41
  • Radiology, Nuclear Medicine and Imaging 136
  • Pulmonary and Respiratory Medicine 124
  • Artificial Intelligence 77
  • Oncology 63
  • Cancer Research 61
Replace Jionghui Gu with:
Jionghui Gu China
Renata Zelić Sweden
Giulia Vatteroni Italy
Bernadette Redd United States
Birgid Schömig‐Markiefka Germany
Yiquan Jiang China
Nicholas Meti Canada
Riham Eiada Canada
Hildegard Toth United States
David Spak United States
Veronica Magni relative to Jionghui Gu China Jionghui Gu's profile →
Citations per field
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Jionghui Gu · 1×
Citations per year

Countries citing papers authored by Veronica Magni

Since Specialization
Citations

This map shows the geographic impact of Veronica Magni'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 Veronica Magni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Veronica Magni more than expected).

Fields of papers citing papers by Veronica Magni

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Veronica Magni. 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 Veronica Magni. The network helps show where Veronica Magni may publish in the future.

Co-authorship network of co-authors of Veronica Magni

This figure shows the co-authorship network connecting the top 25 collaborators of Veronica Magni. A scholar is included among the top collaborators of Veronica Magni 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 Veronica Magni. Veronica Magni is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

12 of 12 papers shown
#WorkIndexed citations
1 19
2 2
3 8
4 11
5 26
6 14
7 19
8 4
9 89
10 1
11 21
12 39

About Veronica Magni

Veronica Magni is a scholar working on Radiology, Nuclear Medicine and Imaging, Pathology and Forensic Medicine and Internal Medicine, having authored 12 papers that have together received 253 indexed citations. Recurring topics across this work include Digital Radiography and Breast Imaging (7 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and Breast Lesions and Carcinomas (5 papers). The work is most often cited by research in Health Informatics (14 citations), Radiology, Nuclear Medicine and Imaging (136 citations) and Cancer Research (61 citations). Veronica Magni has collaborated with scholars based in Italy, United Kingdom and Switzerland. Frequent co-authors include Francesco Sardanelli, Andrea Cozzi, Simone Schiaffino, Moreno Zanardo, Paolo Giorgi Rossi, Nicolò Matteo Luca Battisti, Rubina Manuela Trimboli, Gabriele Rossini, Fleur Kilburn‐Toppin and Isabella Castiglioni. Their work appears in journals such as Radiology, European Radiology and British Journal of 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.

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