Filippo Piccinini
Impact in
- Biophysics top 0.5%
- Cell Image Analysis Techniques
- Advanced Fluorescence Microscopy Techniques
- Oncology top 5%
- Cancer Cells and Metastasis
Papers in
- Biophysics 35
- Cell Image Analysis Techniques 35
- Advanced Fluorescence Microscopy Techniques 6
-
- Image Processing Techniques and Applications 18
- Co-authors
- Alessandro BevilacquaAnna TeseiChiara ArientiMichele ZanoniSpartaco SantiAlice ZamagniR. PolicoPéter Horváth
In The Last Decade
Filippo Piccinini
67 papers receiving 2.3k citations
Hit Papers
Peers
Comparison fields: 5 of 160
- Biophysics 523
- Oncology 550
- Biomedical Engineering 876
- Media Technology 161
- Cancer Research 226
Countries citing papers authored by Filippo Piccinini
This map shows the geographic impact of Filippo Piccinini'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 Filippo Piccinini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Filippo Piccinini more than expected).
Fields of papers citing papers by Filippo Piccinini
This network shows the impact of papers produced by Filippo Piccinini. 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 Filippo Piccinini. The network helps show where Filippo Piccinini may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Filippo Piccinini, 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 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 5 | |
| 5 | 2024 | 8 | |
| 6 | 2023 | 39 | |
| 7 | 2023 | 4 | |
| 8 | 2022 | 44 | |
| 9 | 2021 | 10 | |
| 10 | 2021 | 9 | |
| 11 | 2021 | 1 | |
| 12 | 2021 | 9 | |
| 13 | 2020 | 16 | |
| 14 | 2018 | 65 | |
| 15 | 2017 | 17 | |
| 16 | 2017 | 93 | |
| 17 | 3D tumor spheroid models for in vitro therapeutic screening: a systematic approach to enhance the biological relevance of data obtained Hit paper breakdown → | 2016 | 838 |
| 18 | 2015 | 109 | |
| 19 | 2014 | 6 | |
| 20 | 2012 | 23 |
About Filippo Piccinini
Filippo Piccinini is a scholar working on Biophysics, Media Technology, Computer Vision and Pattern Recognition, Cancer Research and Biomedical Engineering, having authored 70 papers that have together received 2.4k indexed citations. Recurring topics across this work include Cell Image Analysis Techniques (35 papers), Image Processing Techniques and Applications (18 papers), 3D Printing in Biomedical Research (14 papers), Cancer Cells and Metastasis (9 papers), Single-cell and spatial transcriptomics (7 papers), Advanced Fluorescence Microscopy Techniques (6 papers), AI in cancer detection (6 papers) and Advanced Vision and Imaging (6 papers). The work is most often cited by research in Biophysics (523 citations), Oncology (550 citations), Biomedical Engineering (876 citations), Media Technology (161 citations) and Cancer Research (226 citations). Filippo Piccinini has collaborated with scholars based in Italy, Finland and Hungary. Frequent co-authors include Alessandro Bevilacqua, Anna Tesei, Chiara Arienti, Michele Zanoni, Spartaco Santi, Alice Zamagni, R. Polico, Péter Horváth, Giovanni Martinelli and Gabriel Landini. Their work appears in journals such as Computer Methods and Programs in Biomedicine, Computational and Structural Biotechnology Journal, Scientific Reports, Bioinformatics and Sensors.
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.