Paolo Andreini

537 total citations
22 papers, 246 citations indexed

About

Paolo Andreini is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Paolo Andreini has authored 22 papers receiving a total of 246 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computer Vision and Pattern Recognition, 6 papers in Artificial Intelligence and 5 papers in Molecular Biology. Recurrent topics in Paolo Andreini's work include Medical Image Segmentation Techniques (5 papers), AI in cancer detection (3 papers) and Advanced Neural Network Applications (3 papers). Paolo Andreini is often cited by papers focused on Medical Image Segmentation Techniques (5 papers), AI in cancer detection (3 papers) and Advanced Neural Network Applications (3 papers). Paolo Andreini collaborates with scholars based in Italy, Germany and United Kingdom. Paolo Andreini's co-authors include Simone Bonechi, Monica Bianchini, Franco Scarselli, A. Mecocci, Giovanna Maria Dimitri, Andrea Sodi, A. Mecocci, Caterina Graziani, Andrea Garzelli and Alberto Rossi and has published in prestigious journals such as Pattern Recognition, Neurocomputing and Pattern Recognition Letters.

In The Last Decade

Paolo Andreini

21 papers receiving 245 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Paolo Andreini Italy 10 116 76 43 38 28 22 246
Priyadarshini Adyasha Pattanaik India 8 151 1.3× 91 1.2× 87 2.0× 22 0.6× 18 0.6× 20 282
Somsak Choomchuay Thailand 11 134 1.2× 132 1.7× 121 2.8× 25 0.7× 11 0.4× 60 349
Nadia Brancati Italy 12 148 1.3× 173 2.3× 118 2.7× 47 1.2× 37 1.3× 23 363
Aimon Rahman Bangladesh 7 118 1.0× 97 1.3× 93 2.2× 19 0.5× 10 0.4× 8 225
Thomas Wollmann Germany 10 73 0.6× 86 1.1× 39 0.9× 66 1.7× 49 1.8× 14 254
Baochuan Pang China 8 130 1.1× 228 3.0× 105 2.4× 26 0.7× 16 0.6× 31 323
Ashkan Tashk Iran 11 221 1.9× 144 1.9× 110 2.6× 23 0.6× 20 0.7× 35 328
Jiří Borovec Czechia 6 77 0.7× 69 0.9× 29 0.7× 22 0.6× 14 0.5× 10 173
Santanu Roy India 8 135 1.2× 181 2.4× 89 2.1× 35 0.9× 8 0.3× 19 277
Salam Shuleenda Devi India 10 187 1.6× 53 0.7× 35 0.8× 23 0.6× 13 0.5× 21 247

Countries citing papers authored by Paolo Andreini

Since Specialization
Citations

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

Fields of papers citing papers by Paolo Andreini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Paolo Andreini

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

All Works

20 of 20 papers shown
1.
Andreini, Paolo, Marco Tanfoni, Simone Bonechi, & Monica Bianchini. (2025). Leveraging synthetic data for zero–shot and few–shot circle detection in real–world domains. Pattern Recognition. 172. 112407–112407.
2.
Andreini, Paolo, et al.. (2025). A smart virtual keyboard to improve communication of locked-in patients. Computer Standards & Interfaces. 93. 103963–103963. 1 indexed citations
3.
Bonechi, Simone, et al.. (2024). Diff-Props: is Semantics Preserved within a Diffusion Model?. Procedia Computer Science. 246. 5244–5253. 1 indexed citations
4.
Bonechi, Simone, et al.. (2024). An analysis of pre-trained stable diffusion models through a semantic lens. Neurocomputing. 614. 128846–128846. 2 indexed citations
5.
Andreini, Paolo, Simone Bonechi, & Giovanna Maria Dimitri. (2023). Enhancing glomeruli segmentation through cross-species pre-training. Neurocomputing. 563. 126947–126947. 9 indexed citations
6.
Andreini, Paolo, et al.. (2022). A Deep Learning approach for oocytes segmentation and analysis. 327–332. 3 indexed citations
7.
Andreini, Paolo, Simone Bonechi, Monica Bianchini, & Filippo Geraci. (2022). MicroRNA signature for interpretable breast cancer classification with subtype clue. Use Siena air (University of Siena). 3. 100042–100042. 6 indexed citations
8.
Dimitri, Giovanna Maria, Paolo Andreini, Simone Bonechi, et al.. (2022). Deep Learning Approaches for the Segmentation of Glomeruli in Kidney Histopathological Images. Mathematics. 10(11). 1934–1934. 12 indexed citations
9.
Andreini, Paolo & Giovanna Maria Dimitri. (2022). Deep Semantic Segmentation Models in Computer Vision. Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano). 305–314. 1 indexed citations
10.
Andreini, Paolo, et al.. (2021). Nowcasting German GDP: Foreign factors, financial markets, and model averaging. International Journal of Forecasting. 39(1). 298–313. 9 indexed citations
11.
Bonechi, Simone, Paolo Andreini, A. Mecocci, et al.. (2021). Segmentation of Aorta 3D CT Images Based on 2D Convolutional Neural Networks. Electronics. 10(20). 2559–2559. 15 indexed citations
12.
Andreini, Paolo, Simone Bonechi, Caterina Graziani, et al.. (2021). A Two-Stage GAN for High-Resolution Retinal Image Generation and Segmentation. Electronics. 11(1). 60–60. 44 indexed citations
13.
Rossi, Alberto, Simone Bonechi, Paolo Andreini, et al.. (2020). Graph Neural Networks for the Prediction of Protein-Protein Interfaces.. Use Siena air (University of Siena). 127–132. 13 indexed citations
14.
Andreini, Paolo, et al.. (2020). Nowcasting German GDP. London Business School Research Online (London Business School). 1 indexed citations
15.
Andreini, Paolo, Simone Bonechi, Pietro Bongini, et al.. (2020). Deep Learning Techniques for Dragonfly Action Recognition. Use Siena air (University of Siena). 3 indexed citations
16.
Bonechi, Simone, Monica Bianchini, Franco Scarselli, & Paolo Andreini. (2020). Weak supervision for generating pixel–level annotations in scene text segmentation. Pattern Recognition Letters. 138. 1–7. 40 indexed citations
17.
Andreini, Paolo, Simone Bonechi, Monica Bianchini, A. Mecocci, & Franco Scarselli. (2019). Image generation by GAN and style transfer for agar plate image segmentation. Computer Methods and Programs in Biomedicine. 184. 105268–105268. 54 indexed citations
18.
Bonechi, Simone, Paolo Andreini, Monica Bianchini, Akshay Pai, & Franco Scarselli. (2019). Confidence Measures for Deep Learning in Domain Adaptation. Applied Sciences. 9(11). 2192–2192. 2 indexed citations
19.
Andreini, Paolo, Simone Bonechi, Monica Bianchini, Andrea Garzelli, & A. Mecocci. (2016). Automatic image classification for the urinoculture screening. Computers in Biology and Medicine. 70. 12–22. 15 indexed citations
20.
Andreini, Paolo, Simone Bonechi, Monica Bianchini, Andrea Garzelli, & A. Mecocci. (2016). ABLE: An Automated Bacterial Load Estimator for the Urinoculture Screening. Use Siena air (University of Siena). 573–580. 1 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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