Zobrazeno 1 - 10
of 99
pro vyhledávání: '"Manfredo Atzori"'
Autor:
Artur Jurgas, Marek Wodzinski, Marina D’Amato, Jeroen van der Laak, Manfredo Atzori, Henning Müller
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract The problem of artifacts in whole slide image acquisition, prevalent in both clinical workflows and research-oriented settings, necessitates human intervention and re-scanning. Overcoming this challenge requires developing quality control al
Externí odkaz:
https://doaj.org/article/d0b8a91b181c4456bf6f628d162cde29
Autor:
Weronika Celniak, Marek Wodziński, Artur Jurgas, Silvia Burti, Alessandro Zotti, Manfredo Atzori, Henning Müller, Tommaso Banzato
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Abstract The analysis of veterinary radiographic imaging data is an essential step in the diagnosis of many thoracic lesions. Given the limited time that physicians can devote to a single patient, it would be valuable to implement an automated system
Externí odkaz:
https://doaj.org/article/397073fe8c9141759b2feae3746869fe
Autor:
Anastasiia Rozhyna, Gábor Márk Somfai, Manfredo Atzori, Delia Cabrera DeBuc, Amr Saad, Jay Zoellin, Henning Müller
Publikováno v:
Diagnostics, Vol 14, Iss 15, p 1668 (2024)
Artificial intelligence has transformed medical diagnostic capabilities, particularly through medical image analysis. AI algorithms perform well in detecting abnormalities with a strong performance, enabling computer-aided diagnosis by analyzing the
Externí odkaz:
https://doaj.org/article/f2f8112fcc6646c7bd1bba525b15f3b0
Autor:
Federico Del Pup, Manfredo Atzori
Publikováno v:
IEEE Access, Vol 11, Pp 144180-144203 (2023)
Over the last decade, deep learning applications in biomedical research have exploded, demonstrating their ability to often outperform previous machine learning approaches in various tasks. However, training deep learning models for biomedical applic
Externí odkaz:
https://doaj.org/article/f5510244ff074a99a9cb8788146615f1
Autor:
Alessandro Scano, Nestor Jarque-Bou, Cristina Brambilla, Manfredo Atzori, Andrea D'Avella, Henning Muller
Publikováno v:
IEEE Access, Vol 11, Pp 108544-108560 (2023)
Hand grasp patterns are the results of complex kinematic-muscular coordination and synergistic control might help reducing the dimensionality of the motor control space at the hand level. Kinematic-muscular synergies combining muscle and kinematic ha
Externí odkaz:
https://doaj.org/article/f05660f9bbe84f10a7a7f2a307df4495
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Abstract Several challenges prevent extracting knowledge from biomedical resources, including data heterogeneity and the difficulty to obtain and collaborate on data and annotations by medical doctors. Therefore, flexibility in their representation a
Externí odkaz:
https://doaj.org/article/186ad58e35bc42429208c5f181dbe0b8
Autor:
Niccolò Marini, Stefano Marchesin, Sebastian Otálora, Marek Wodzinski, Alessandro Caputo, Mart van Rijthoven, Witali Aswolinskiy, John-Melle Bokhorst, Damian Podareanu, Edyta Petters, Svetla Boytcheva, Genziana Buttafuoco, Simona Vatrano, Filippo Fraggetta, Jeroen van der Laak, Maristella Agosti, Francesco Ciompi, Gianmaria Silvello, Henning Muller, Manfredo Atzori
Publikováno v:
npj Digital Medicine, Vol 5, Iss 1, Pp 1-18 (2022)
Abstract The digitalization of clinical workflows and the increasing performance of deep learning algorithms are paving the way towards new methods for tackling cancer diagnosis. However, the availability of medical specialists to annotate digitized
Externí odkaz:
https://doaj.org/article/68d27b5d6ac64a1abbd9bf6676cecdcf
Autor:
Laura Menotti, Gianmaria Silvello, Manfredo Atzori, Svetla Boytcheva, Francesco Ciompi, Giorgio Maria Di Nunzio, Filippo Fraggetta, Fabio Giachelle, Ornella Irrera, Stefano Marchesin, Niccolò Marini, Henning Müller, Todor Primov
Publikováno v:
Journal of Pathology Informatics, Vol 14, Iss , Pp 100332- (2023)
Computational pathology can significantly benefit from ontologies to standardize the employed nomenclature and help with knowledge extraction processes for high-quality annotated image datasets. The end goal is to reach a shared model for digital pat
Externí odkaz:
https://doaj.org/article/81eb50cf871b4bf3a039110465600863
Autor:
Niccolò Marini, Sebastian Otalora, Marek Wodzinski, Selene Tomassini, Aldo Franco Dragoni, Stephane Marchand-Maillet, Juan Pedro Dominguez Morales, Lourdes Duran-Lopez, Simona Vatrano, Henning Müller, Manfredo Atzori
Publikováno v:
Journal of Pathology Informatics, Vol 14, Iss , Pp 100183- (2023)
Computational pathology targets the automatic analysis of Whole Slide Images (WSI). WSIs are high-resolution digitized histopathology images, stained with chemical reagents to highlight specific tissue structures and scanned via whole slide scanners.
Externí odkaz:
https://doaj.org/article/79b73b6c8c4f4413b7207b6afa5a21dc
Publikováno v:
BMC Medical Imaging, Vol 21, Iss 1, Pp 1-14 (2021)
Abstract Background One challenge to train deep convolutional neural network (CNNs) models with whole slide images (WSIs) is providing the required large number of costly, manually annotated image regions. Strategies to alleviate the scarcity of anno
Externí odkaz:
https://doaj.org/article/82eb75592e4e4648b3ac96f8b4e2d898