5 papers
Beyond the Generative Learning Trilemma: Generative Model Assessment in Data Scarcity Domains
Marco Salmè, Lorenzo Tronchin, Rosa Sicilia +2
Data scarcity remains a critical bottleneck impeding technological advancements across various domains, including but not limited to medicine and precision agriculture. To address…
Beyond a Single Mode: GAN Ensembles for Diverse Medical Data Generation
Lorenzo Tronchin, Tommy Löfstedt, Paolo Soda +1
The advancement of generative AI, particularly in medical imaging, confronts the trilemma of ensuring high fidelity, diversity, and efficiency in synthetic data generation. While G…
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…
Using Synthetic Images to Augment Small Medical Image Datasets
Minh H. Vu, Lorenzo Tronchin, Tufve Nyholm +1
Recent years have witnessed a growing academic and industrial interest in deep learning (DL) for medical imaging. To perform well, DL models require very large labeled datasets. Ho…
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…