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Vito Paolo Pastore - IEEE Xplore Author Profile
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Vito Paolo Pastore

Also published under: Vito P. Pastore, V. P. Pastore

Publications
10
Citations
9
Publications by Year
20152024
Co-Authors:
Show All Co-Authors (27)
Image of author Vito Paolo Pastore

Vito Paolo Pastore

Also published under: Vito P. Pastore, V. P. Pastore

Affiliation

MaLGa-DIBRIS
Università degli Studi di Genova
Genoa, Italy

Publication Topics

Biography

Vito Paolo Pastore received the M.S. degree in bioengineering and the Ph.D. degree in bioengineering and robotics from the University of Genova, in 2014 and 2018, respectively. He is currently an Assistant Professor with the Machine Learning Genoa Center (MaLGa), DIBRIS, University of Genova. From 2018 to 2020, he was a Postdoctoral Researcher with IBM Research Almaden, working on machine learning for cellular image analysis; and the Istituto Italiano di Tecnologia in Genova, from 2020 to 2022, working on visual perception for robotics. His main research interests include learning with imperfect data, unsupervised learning, model debiasing, machine learning, deep learning for biological and medical imaging, industrial applications, and mach...
Publications
10
Citations
9
Publications by Year
20152024
Co-Authors:
Show All Co-Authors (27)
Author's Published Works

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Ensembles of Deep Neural Networks for the Automatic Detection of Building Facade Defects From Images

Matteo Interlando;Maria Giovanna Pacifico;Antonio Novellino;Vito Paolo Pastore

IEEE Access
Year: 2024 | Volume: 12 | Journal Article |
Preserving the value of buildings and ensuring performance levels within acceptable parameters throughout their lifespan necessitates constant monitoring. In recent years, artificial intelligence has provided a valuable supplement to conventional inspection practices, potentially offering a supporting tool for building maintenance in smart cities. Exploiting machine learning algorithms for detecti...Show More

Ensembles of Deep Neural Networks for the Automatic Detection of Building Facade Defects From Images

Matteo Interlando;Maria Giovanna Pacifico;Antonio Novellino;Vito Paolo Pastore

Year: 2024 | Volume: 12 | Journal Article |

In Domain Transfer Learning for Prostate MRI Segmentation

Larbi Touijer;Matteo Santacesaria;Francesca Odone;Vito Paolo Pastore

2024 14th International Conference on Pattern Recognition Systems (ICPRS)
Year: 2024 | Conference Paper |
In recent years, deep learning has been widely applied to different medical image analysis tasks. However, large-scale annotated datasets are typically unavailable in such a domain, potentially hindering deep learning applications. Transfer learning with a fine-tuning framework is a commonly adopted solution to this issue, exploiting large-scale natural image datasets (e.g., ImageNet) to pre-train...Show More

In Domain Transfer Learning for Prostate MRI Segmentation

Larbi Touijer;Matteo Santacesaria;Francesca Odone;Vito Paolo Pastore

Year: 2024 | Conference Paper |

Is In-Domain Data Beneficial in Transfer Learning for Landmarks Detection in X-Ray Images?

Roberto Di Via;Matteo Santacesaria;Francesca Odone;Vito Paolo Pastore

2024 IEEE International Symposium on Biomedical Imaging (ISBI)
Year: 2024 | Conference Paper |
In recent years, deep learning has emerged as a promising technique for medical image analysis. However, this application domain is likely to suffer from a limited availability of large public datasets and annotations. A common solution to these challenges in deep learning is the usage of a transfer learning framework, typically with a fine-tuning protocol, where a large-scale source dataset is us...Show More

Is In-Domain Data Beneficial in Transfer Learning for Landmarks Detection in X-Ray Images?

Roberto Di Via;Matteo Santacesaria;Francesca Odone;Vito Paolo Pastore

Year: 2024 | Conference Paper |

A Control-Oriented Highway Traffic Model with Multiple Clusters of CAVs

T. Chaanine;A. Ferrando;C. Pasquale;V. P. Pastore;M. Sadeghi Garjan;S. Siri

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
The presented work falls in the field of modelling the presence of multiple clusters of connected autonomous vehicles (CAVs), i.e., groups of CAVs in traffic flow that, if properly controlled, can positively influence traffic behavior by acting as actuators of specific control strategies. An extended version of the well-known Cell Transmission Model (CTM) is used as a traffic model, where the cell...Show More

A Control-Oriented Highway Traffic Model with Multiple Clusters of CAVs

T. Chaanine;A. Ferrando;C. Pasquale;V. P. Pastore;M. Sadeghi Garjan;S. Siri

Year: 2023 | Conference Paper |

An efficient deep learning approach to identify dynamics in in vitro neural networks

Vito Paolo Pastore;Giulia Parodi;Martina Brofiga;Paolo Massobrio;Michela Chiappalone;Francesca Odone;Sergio Martinoia

2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Year: 2023 | Conference Paper |
Understanding and discriminating the spatiotemporal patterns of activity generated by in vitro and in vivo neuronal networks is a fundamental task in neuroscience and neuroengineering. The state-of-the-art algorithms to describe the neuronal activity mostly rely on global and local well-established spike and burst-related parameters. However, they are not able to capture slight differences in the ...Show More

An efficient deep learning approach to identify dynamics in in vitro neural networks

Vito Paolo Pastore;Giulia Parodi;Martina Brofiga;Paolo Massobrio;Michela Chiappalone;Francesca Odone;Sergio Martinoia

Year: 2023 | Conference Paper |

Incorporating Diagnostic Prior with Segmentation: A Deep Learning Pipeline for the Automatic Classification of Autoimmune Bullous Skin Diseases

Vito Paolo Pastore;Larbi Touijer;Niccolò Capurro;Emanuele Cozzani;Giulia Gasparini;Aurora Parodi;Francesca Odone

2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
The term AutoImmune Bullous Diseases (AIBDs) refers to a wide group of skin disorders, in which autoantibodies are developed and directed against proteins of the epidermis and the basal membrane. The correct diagnosis and classification of AIBDs require the analysis of ImmunoFluorescence (IF) skin images. Up to now, it can only be performed by operators with great clinical expertise and it is limi...Show More

Incorporating Diagnostic Prior with Segmentation: A Deep Learning Pipeline for the Automatic Classification of Autoimmune Bullous Skin Diseases

Vito Paolo Pastore;Larbi Touijer;Niccolò Capurro;Emanuele Cozzani;Giulia Gasparini;Aurora Parodi;Francesca Odone

Year: 2023 | Conference Paper |

Efficient Unsupervised Learning for Plankton Images

Paolo Didier Alfano;Marco Rando;Marco Letizia;Francesca Odone;Lorenzo Rosasco;Vito Paolo Pastore

2022 26th International Conference on Pattern Recognition (ICPR)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
Monitoring plankton populations in situ is fundamental to preserve the aquatic ecosystem. Plankton microorganisms are in fact susceptible of minor environmental perturbations, that can reflect into consequent morphological and dynamical modifications. Nowadays, the availability of advanced automatic or semi-automatic acquisition systems has been allowing the production of an increasingly large amo...Show More

Efficient Unsupervised Learning for Plankton Images

Paolo Didier Alfano;Marco Rando;Marco Letizia;Francesca Odone;Lorenzo Rosasco;Vito Paolo Pastore

Year: 2022 | Conference Paper |

A toolbox for dynamic and connectivity analysis of neuronal spike trains data

Vito Paolo Pastore;Aleksandar Godjoski;Sergio Martinoia;Paolo Massobrio

2017 8th International IEEE/EMBS Conference on Neural Engineering (NER)
Year: 2017 | Conference Paper |
Cited by: Papers (1)
Thanks to recent improvements in the neurotechnology, parallel recordings with an ever-increasing number of micro-transducers are now available to monitor the neuronal spiking activity of large-scale neuronal networks. At the same time, continuous improvements are required to develop computationally efficient software for processing and analyzing such huge amounts of data. In this work, we present...Show More

A toolbox for dynamic and connectivity analysis of neuronal spike trains data

Vito Paolo Pastore;Aleksandar Godjoski;Sergio Martinoia;Paolo Massobrio

Year: 2017 | Conference Paper |

A new connectivity toolbox to infer topological features of in-vitro neural networks

Vito Paolo Pastore;Daniele Poli;Sergio Martinoia;Paolo Massobrio

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2015 | Conference Paper |
Cited by: Papers (1)
A detailed analysis of functional connectivity of in vitro neural networks, as well as the possibility to understand the interplay between topology, structure, function and dynamics, is very important for better understanding how the nervous system represents and stores the information. Thus, we developed an informatics toolbox to infer functional connectivity in in-vitro neuronal networks. To pro...Show More

A new connectivity toolbox to infer topological features of in-vitro neural networks

Vito Paolo Pastore;Daniele Poli;Sergio Martinoia;Paolo Massobrio

Year: 2015 | Conference Paper |

Functional connectivity in cultured cortical networks during development: Comparison between correlation and information theory-based algorithms

Daniele Poli;Vito P. Pastore;Paolo Massobrio;Sergio Martinoia

2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)
Year: 2015 | Conference Paper |
Goal of this work is to present a general approach to estimate functional connectivity in in vitro cortical networks coupled to Micro-Electrode Array (MEAs). Specifically, we developed and optimized a Partial Correlation (PC) based algorithm and we compared it to Cross Correlation (CC) and Transfer Entropy (TE) methods. First, we applied the algorithms to simulated networks with different average ...Show More

Functional connectivity in cultured cortical networks during development: Comparison between correlation and information theory-based algorithms

Daniele Poli;Vito P. Pastore;Paolo Massobrio;Sergio Martinoia

Year: 2015 | Conference Paper |

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