Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Valvano, Gabriele"'
Autor:
Valvano, Gabriele, Agostino, Antonino, De Magistris, Giovanni, Graziano, Antonino, Veneri, Giacomo
Publikováno v:
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 5354-5363
Training supervised deep neural networks that perform defect detection and segmentation requires large-scale fully-annotated datasets, which can be hard or even impossible to obtain in industrial environments. Generative AI offers opportunities to en
Externí odkaz:
http://arxiv.org/abs/2401.03152
Publikováno v:
Journal of Machine Learning for Biomedical Imaging (MELBA), 2022
Thanks to their ability to learn flexible data-driven losses, Generative Adversarial Networks (GANs) are an integral part of many semi- and weakly-supervised methods for medical image segmentation. GANs jointly optimise a generator and an adversarial
Externí odkaz:
http://arxiv.org/abs/2108.11926
Collecting large-scale medical datasets with fine-grained annotations is time-consuming and requires experts. For this reason, weakly supervised learning aims at optimising machine learning models using weaker forms of annotations, such as scribbles,
Externí odkaz:
http://arxiv.org/abs/2108.11900
Thanks to their ability to learn data distributions without requiring paired data, Generative Adversarial Networks (GANs) have become an integral part of many computer vision methods, including those developed for medical image segmentation. These me
Externí odkaz:
http://arxiv.org/abs/2108.12280
Autor:
Liu, Xiao, Thermos, Spyridon, Valvano, Gabriele, Chartsias, Agisilaos, O'Neil, Alison, Tsaftaris, Sotirios A.
A recent spate of state-of-the-art semi- and un-supervised solutions disentangle and encode image "content" into a spatial tensor and image appearance or "style" into a vector, to achieve good performance in spatially equivariant tasks (e.g. image-to
Externí odkaz:
http://arxiv.org/abs/2008.12378
Publikováno v:
IEEE Transactions on Medical Imaging, 2021
Large, fine-grained image segmentation datasets, annotated at pixel-level, are difficult to obtain, particularly in medical imaging, where annotations also require expert knowledge. Weakly-supervised learning can train models by relying on weaker for
Externí odkaz:
http://arxiv.org/abs/2007.01152
Publikováno v:
Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data. DART 2019, MIL3ID 2019. Lecture Notes in Computer Science, vol 11795 pp 11-19. Springer, Cham
There has been an increasing focus in learning interpretable feature representations, particularly in applications such as medical image analysis that require explainability, whilst relying less on annotated data (since annotations can be tedious and
Externí odkaz:
http://arxiv.org/abs/1908.11330
Autor:
Valvano, Gabriele, Leo, Andrea, Della Latta, Daniele, Martini, Nicola, Santini, Gianmarco, Chiappino, Dante, Ricciardi, Emiliano
Recent research put a big effort in the development of deep learning architectures and optimizers obtaining impressive results in areas ranging from vision to language processing. However little attention has been addressed to the need of a methodolo
Externí odkaz:
http://arxiv.org/abs/1810.12142
Autor:
Valvano, Gabriele, Martini, Nicola, Leo, Andrea, Santini, Gianmarco, Della Latta, Daniele, Ricciardi, Emiliano, Chiappino, Dante
Skull-stripping methods aim to remove the non-brain tissue from acquisition of brain scans in magnetic resonance (MR) imaging. Although several methods sharing this common purpose have been presented in literature, they all suffer from the great vari
Externí odkaz:
http://arxiv.org/abs/1810.10853
Autor:
Valvano, Gabriele, Santini, Gianmarco, Martini, Nicola, Ripoli, Andrea, Iacconi, Chiara, Chiappino, Dante, Della Latta, Daniele
Cluster of microcalcifications can be an early sign of breast cancer. In this paper we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work we used 283 mammogr
Externí odkaz:
http://arxiv.org/abs/1809.03788