Search

Your search keyword '"Allegretti, Stefano"' showing total 25 results
Start Over Author "Allegretti, Stefano" Publication Year Range Last 50 years

Refine your results

more »

more »

25 results on '"Allegretti, Stefano"'

1. Quest for Speed: The Epic Saga of Record-Breaking on OpenCV Connected Components Extraction

Author

Bolelli, Federico, Allegretti, Stefano, Grana, Costantino, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Mazzeo, Pier Luigi, editor, Frontoni, Emanuele, editor, Sclaroff, Stan, editor, and Distante, Cosimo, editor

Published
2022
Full Text
View/download PDF

2. Connected Components Labeling on Bitonal Images

Author

Bolelli, Federico, Allegretti, Stefano, Grana, Costantino, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sclaroff, Stan, editor, Distante, Cosimo, editor, Leo, Marco, editor, Farinella, Giovanni M., editor, and Tombari, Federico, editor

Published
2022
Full Text
View/download PDF

3. A Heuristic-Based Decision Tree for Connected Components Labeling of 3D Volumes: Implementation and Reproducibility Notes

Author

Bolelli, Federico, Allegretti, Stefano, Grana, Costantino, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Kerautret, Bertrand, editor, Colom, Miguel, editor, Krähenbühl, Adrien, editor, Lopresti, Daniel, editor, Monasse, Pascal, editor, and Talbot, Hugues, editor

Published
2021
Full Text
View/download PDF

5. A Block-Based Union-Find Algorithm to Label Connected Components on GPUs

Author

Allegretti, Stefano, Bolelli, Federico, Cancilla, Michele, Grana, Costantino, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ricci, Elisa, editor, Rota Bulò, Samuel, editor, Snoek, Cees, editor, Lanz, Oswald, editor, Messelodi, Stefano, editor, and Sebe, Nicu, editor

Published
2019
Full Text
View/download PDF

6. Skin Lesion Segmentation Ensemble with Diverse Training Strategies

Author

Canalini, Laura, Pollastri, Federico, Bolelli, Federico, Cancilla, Michele, Allegretti, Stefano, Grana, Costantino, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Vento, Mario, editor, and Percannella, Gennaro, editor

Published
2019
Full Text
View/download PDF

7. How Does Connected Components Labeling with Decision Trees Perform on GPUs?

Author

Allegretti, Stefano, Bolelli, Federico, Cancilla, Michele, Pollastri, Federico, Canalini, Laura, Grana, Costantino, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Vento, Mario, editor, and Percannella, Gennaro, editor

Published
2019
Full Text
View/download PDF

11. Inferior Alveolar Canal Automatic Detection with Deep Learning CNNs on CBCTs: Development of a Novel Model and Release of Open-Source Dataset and Algorithm

Author

Di Bartolomeo, Mattia, primary, Pellacani, Arrigo, additional, Bolelli, Federico, additional, Cipriano, Marco, additional, Lumetti, Luca, additional, Negrello, Sara, additional, Allegretti, Stefano, additional, Minafra, Paolo, additional, Pollastri, Federico, additional, Nocini, Riccardo, additional, Colletti, Giacomo, additional, Chiarini, Luigi, additional, Grana, Costantino, additional, and Anesi, Alexandre, additional

Published
2023
Full Text
View/download PDF

17. The DeepHealth Toolkit: A unified framework to boost biomedical applications

Author

Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Barcelona Supercomputing Center, Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions, Cancilla, Michele, Canalini, Laura, Bolelli, Federico, Allegretti, Stefano, Carrión Ponz, Salvador, Paredes Palacios, Roberto, Gómez Adrián, Jon A., Leo, Simone, Piras, Marco Enrico, Pireddu, Luca, Badouh, Asaf, Marco-Sola, Santiago, Álvarez Martí, Lluc, Moreto Planas, Miquel, Grana, Costantino, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Barcelona Supercomputing Center, Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions, Cancilla, Michele, Canalini, Laura, Bolelli, Federico, Allegretti, Stefano, Carrión Ponz, Salvador, Paredes Palacios, Roberto, Gómez Adrián, Jon A., Leo, Simone, Piras, Marco Enrico, Pireddu, Luca, Badouh, Asaf, Marco-Sola, Santiago, Álvarez Martí, Lluc, Moreto Planas, Miquel, and Grana, Costantino

Abstract

Given the overwhelming impact of machine learning on the last decade, several libraries and frameworks have been developed in recent years to simplify the design and training of neural networks, providing array-based programming, automatic differentiation and user-friendly access to hardware accelerators. None of those tools, however, was designed with native and transparent support for Cloud Computing or heterogeneous High-Performance Computing (HPC). The DeepHealth Toolkit is an open source Deep Learning toolkit aimed at boosting productivity of data scientists operating in the medical field by providing a unified framework for the distributed training of neural networks, which is able to leverage hybrid HPC and cloud environments in a transparent way for the user. The toolkit is composed of a Computer Vision library, a Deep Learning library, and a front-end for non-expert users; all of the components are focused on the medical domain, but they are general purpose and can be applied to any other field. In this paper, the principles driving the design of the DeepHealth libraries are described, along with details about the implementation and the interaction between the different elements composing the toolkit. Finally, experiments on common benchmarks prove the efficiency of each separate component and of the DeepHealth Toolkit overall., This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825111, DeepHealth Project. Lluc Alvarez has been supported by the Spanish Ministry of Economy, Industry and Competitiveness under the Juan de la Cierva Formacion fellowship No. FJCI-2016-30984. Miquel Moreto has been supported by the Spanish Ministry of Economy, Industry and Competitiveness under the Ramon y Cajal fellowship No. RYC-2016-21104., Peer Reviewed, Postprint (author's final draft)

Published
2021

19. The DeepHealth Toolkit: A Unified Framework to Boost Biomedical Applications

Author

Cancilla, Michele, primary, Canalini, Laura, additional, Bolelli, Federico, additional, Allegretti, Stefano, additional, Carrion, Salvador, additional, Paredes, Roberto, additional, Gomez, Jon A., additional, Leo, Simone, additional, Piras, Marco Enrico, additional, Pireddu, Luca, additional, Badouh, Asaf, additional, Marco-Sola, Santiago, additional, Alvarez, Lluc, additional, Moreto, Miquel, additional, and Grana, Costantino, additional

Published
2021
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results