22 citations · 22 across the 2 of their papers we have counts for
9 papers
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…
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.…