5 citations · 10 across the 4 of their papers we have counts for
4 papers
Any-to-Any Vision-Language Model for Multimodal X-ray Imaging and Radiological Report Generation
Daniele Molino, Francesco di Feola, Linlin Shen +2
Generative models have revolutionized Artificial Intelligence (AI), particularly in multimodal applications. However, adapting these models to the medical domain poses unique chall…
Texture-Aware StarGAN for CT data harmonisation
Francesco Di Feola, Ludovica Pompilio, Cecilia Assolito +2
Computed Tomography (CT) plays a pivotal role in medical diagnosis; however, variability across reconstruction kernels hinders data-driven approaches, such as deep learning models,…
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…
XGeM: A Multi-Prompt Foundation Model for Multimodal Medical Data Generation
Daniele Molino, Francesco Di Feola, Eliodoro Faiella +5
The adoption of Artificial Intelligence in medical imaging holds great promise, yet it remains hindered by challenges such as data scarcity, privacy concerns, and the need for robu…