82 citations · 380 across the 53 of their papers we have counts for
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Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging
Elena Mulero Ayllón, Massimiliano Mantegna, Linlin Shen +3
Accurate lung tumor segmentation is crucial for improving diagnosis, treatment planning, and patient outcomes in oncology. However, the complexity of tumor morphology, size, and lo…
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…
Augmented Intelligence for Multimodal Virtual Biopsy in Breast Cancer Using Generative Artificial Intelligence
Aurora Rofena, Claudia Lucia Piccolo, Bruno Beomonte Zobel +2
Full-Field Digital Mammography (FFDM) is the primary imaging modality for routine breast cancer screening; however, its effectiveness is limited in patients with dense breast tissu…
Multi-stage intermediate fusion for multimodal learning to classify non-small cell lung cancer subtypes from CT and PET
Fatih Aksu, Fabrizia Gelardi, Arturo Chiti +1
Accurate classification of histological subtypes of non-small cell lung cancer (NSCLC) is essential in the era of precision medicine, yet current invasive techniques are not always…
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 Deep Learning Approach for Virtual Contrast Enhancement in Contrast Enhanced Spectral Mammography
Aurora Rofena, Valerio Guarrasi, Marina Sarli +4
Contrast Enhanced Spectral Mammography (CESM) is a dual-energy mammographic imaging technique that first needs intravenously administration of an iodinated contrast medium; then, i…
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…
Multi-objective optimization determines when, which and how to fuse deep networks: an application to predict COVID-19 outcomes
Valerio Guarrasi, Paolo Soda
The COVID-19 pandemic has caused millions of cases and deaths and the AI-related scientific community, after being involved with detecting COVID-19 signs in medical images, has bee…
AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study
Paolo Soda, Natascha Claudia D'Amico, Jacopo Tessadori +25
Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very…