30 citations · 50 across the 9 of their papers we have counts for
4 papers · 1 filter
Whole-Body Image-to-Image Translation for a Virtual Scanner in a Healthcare Digital Twin
Valerio Guarrasi, Francesco Di Feola, Rebecca Restivo +2
Generating positron emission tomography (PET) images from computed tomography (CT) scans via deep learning offers a promising pathway to reduce radiation exposure and costs associa…
Multi-Dataset Multi-Task Learning for COVID-19 Prognosis
Filippo Ruffini, Lorenzo Tronchin, Zhuoru Wu +4
In the fight against the COVID-19 pandemic, leveraging artificial intelligence to predict disease outcomes from chest radiographic images represents a significant scientific aim. T…
Multi-Scale Texture Loss for CT denoising with GANs
Francesco Di Feola, Lorenzo Tronchin, Valerio Guarrasi +1
Generative Adversarial Networks (GANs) have proved as a powerful framework for denoising applications in medical imaging. However, GAN-based denoising algorithms still suffer from…
A comparative study between paired and unpaired Image Quality Assessment in Low-Dose CT Denoising
Francesco Di Feola, Lorenzo Tronchin, Paolo Soda
The current deep learning approaches for low-dose CT denoising can be divided into paired and unpaired methods. The former involves the use of well-paired datasets, whilst the latt…