117 citations · 177 across the 11 of their papers we have counts for
12 papers
Controllable Image Synthesis of Industrial Data Using Stable Diffusion
Gabriele Valvano, Antonino Agostino, Giovanni De Magistris +2
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 obtai…
Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training
Gabriele Valvano, Andrea Leo, Sotirios A. Tsaftaris
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 metho…
Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning
Gabriele Valvano, Andrea Leo, Sotirios A. Tsaftaris
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…
Re-using Adversarial Mask Discriminators for Test-time Training under Distribution Shifts
Gabriele Valvano, Andrea Leo, Sotirios A. Tsaftaris
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 im…
Measuring the Biases and Effectiveness of Content-Style Disentanglement
Xiao Liu, Spyridon Thermos, Gabriele Valvano +3
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…
Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates
Gabriele Valvano, Andrea Leo, Sotirios A. Tsaftaris
Large, fine-grained image segmentation datasets, annotated at pixel-level, are difficult to obtain, particularly in medical imaging, where annotations also require expert knowledge…
Temporal Consistency Objectives Regularize the Learning of Disentangled Representations
Gabriele Valvano, Agisilaos Chartsias, Andrea Leo +1
There has been an increasing focus in learning interpretable feature representations, particularly in applications such as medical image analysis that require explainability, whils…
Training of a Skull-Stripping Neural Network with efficient data augmentation
Gabriele Valvano, Nicola Martini, Andrea Leo +4
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…
Unsupervised Data Selection for Supervised Learning
Gabriele Valvano, Andrea Leo, Daniele Della Latta +4
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.…
Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging
Gabriele Valvano, Gianmarco Santini, Nicola Martini +4
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 segment…
Synthetic contrast enhancement in cardiac CT with Deep Learning
Gianmarco Santini, Lorena M. Zumbo, Nicola Martini +6
In Europe the 20% of the CT scans cover the thoracic region. The acquired images contain information about the cardiovascular system that often remains latent due to the lack of co…
An automatic deep learning approach for coronary artery calcium segmentation
G. Santini, D. Della Latta, N. Martini +6
Coronary artery calcium (CAC) is a significant marker of atherosclerosis and cardiovascular events. In this work we present a system for the automatic quantification of calcium sco…