Deborah Fazzini

460 total citations
16 papers, 211 citations indexed

About

Deborah Fazzini is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Biomedical Engineering. According to data from OpenAlex, Deborah Fazzini has authored 16 papers receiving a total of 211 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Radiology, Nuclear Medicine and Imaging, 5 papers in Artificial Intelligence and 3 papers in Biomedical Engineering. Recurrent topics in Deborah Fazzini's work include Radiomics and Machine Learning in Medical Imaging (9 papers), AI in cancer detection (4 papers) and MRI in cancer diagnosis (3 papers). Deborah Fazzini is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (9 papers), AI in cancer detection (4 papers) and MRI in cancer diagnosis (3 papers). Deborah Fazzini collaborates with scholars based in Italy, Sweden and Switzerland. Deborah Fazzini's co-authors include Sergio Papa, Marco Alì, Maurizio Cè, Michaela Cellina, Giancarlo Oliva, Simona Ibba, Giovanni Irmici, Paolo Soda, Domiziana Santucci and Domenico Albano and has published in prestigious journals such as SHILAP Revista de lepidopterología, Pattern Recognition and Applied Sciences.

In The Last Decade

Deborah Fazzini

14 papers receiving 208 citations

Peers

Deborah Fazzini

RNMI

PRM

Giovanni Irmici Italy

J. Buurman Netherlands

Jiayi Yin China

Fan Tang China

Andreas Maier Germany

Nicola Altini Italy

Haiyan Du China

Trupti Kolur India

Ran Gu China

Jim Diamond United Kingdom

Giovanni Irmici Italy

Deborah Fazzini

163 ×1.8

RNMI

57 ×0.8

72 ×1.7

76 ×2.0

61 ×1.8

PRM

Citations per year, relative to Deborah Fazzini Deborah Fazzini (= 1×) peers Giovanni Irmici

Countries citing papers authored by Deborah Fazzini

Since Specialization

Citations

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

Fields of papers citing papers by Deborah Fazzini

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Deborah Fazzini

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

All Works

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16 of 16 papers shown

Darvizeh, Fatemeh, Deborah Fazzini, Alessandro Maiocchi, et al.. (2025). Neural Network Models for Prostate Zones Segmentation in Magnetic Resonance Imaging. Information. 16(3). 186–186. 1 indexed citations

Darvizeh, Fatemeh, Gabriele Gianini, Ernesto Damiani, et al.. (2025). Exploring UNet-based models for prostate lesion segmentation from multi-sequence MRI (T2W, ADC, DWI). World Wide Web. 29(1).

Darvizeh, Fatemeh, et al.. (2025). Structured Transformation of Unstructured Prostate MRI Reports Using Large Language Models. Tomography. 11(6). 69–69.

Guarrasi, Valerio, Domenico Albano, Eliodoro Faiella, et al.. (2024). Multimodal explainability via latent shift applied to COVID-19 stratification. Pattern Recognition. 156. 110825–110825. 19 indexed citations

Cordelli, Ermanno, Paolo Soda, Christian Salvatore, et al.. (2024). Machine learning predicts pulmonary Long Covid sequelae using clinical data. BMC Medical Informatics and Decision Making. 24(1). 359–359. 2 indexed citations

Cellina, Michaela, Maurizio Cè, Andrea Cozzi, et al.. (2024). Thymic Hyperplasia and COVID-19 Pulmonary Sequelae: A Bicentric CT-Based Follow-Up Study. Applied Sciences. 14(9). 3930–3930. 1 indexed citations

Cellina, Michaela, Maurizio Cè, Marco Alì, et al.. (2023). Digital Twins: The New Frontier for Personalized Medicine?. Applied Sciences. 13(13). 7940–7940. 72 indexed citations

Alì, Marco, et al.. (2023). Adoption of AI in Oncological Imaging: Ethical, Regulatory, and Medical-Legal Challenges. Critical Reviews™ in Oncogenesis. 29(2). 29–35. 2 indexed citations

Bologna, Marco, Michaela Cellina, Maurizio Cè, et al.. (2023). Teeth Segmentation in Panoramic Dental X-ray Using Mask Regional Convolutional Neural Network. Applied Sciences. 13(13). 7947–7947. 23 indexed citations

10.

Magni, Veronica, Matteo Interlenghi, Andrea Cozzi, et al.. (2022). Development and Validation of an AI-driven Mammographic Breast Density Classification Tool Based on Radiologist Consensus. Radiology Artificial Intelligence. 4(2). e210199–e210199. 26 indexed citations

11.

Cè, Maurizio, et al.. (2022). Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis—a narrative review. SHILAP Revista de lepidopterología. 3(6). 795–816. 31 indexed citations

12.

Alì, Marco, Natascha Claudia D’Amico, Matteo Interlenghi, et al.. (2021). A Decision Support System Based on BI-RADS and Radiomic Classifiers to Reduce False Positive Breast Calcifications at Digital Breast Tomosynthesis: A Preliminary Study. Applied Sciences. 11(6). 2503–2503. 6 indexed citations

13.

D’Amico, Natascha Claudia, Enzo Grossi, Giovanni Valbusa, et al.. (2020). A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI. European Radiology Experimental. 4(1). 5–5. 16 indexed citations

14.

D’Amico, Natascha Claudia, Rosa Sicilia, Ermanno Cordelli, et al.. (2019). Early radiomics experiences in predicting CyberKnife response in acoustic neuroma. 8(3). 11–13. 2 indexed citations

15.

Merone, Mario, Rosa Sicilia, Ermanno Cordelli, et al.. (2019). Tackling imbalance radiomics in acoustic neuroma. International Journal of Data Mining and Bioinformatics. 22(4). 365–365. 6 indexed citations

16.

Sicilia, Rosa, Ermanno Cordelli, Giovanni Valbusa, et al.. (2018). Radiomics for Predicting CyberKnife response in acoustic neuroma: a pilot study. 4 indexed citations

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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