Although largely preventable, cardiovascular disease (CVD) is the leading cause of death worldwide. Synthetic data generation (SDG) of health data can facilitate data sharing for research and development of personalised cardiovascular disease prevention tools. Current state-of-the-art SDG methods are limited to cross-s...
A. Bertgren, F. Öhberg, P. Soda et al.· bioRxiv· 0 citations
Tumor segmentation is a core task in medical image analysis, with direct implications for diagnosis, treatment planning, and disease monitoring. Whether promptable foundation models are mature enough for heterogeneous oncological scenarios remains an open question. We present a multi-cancer benchmark spanning lung, liv...
M. Tortora, Elena Mulero Ayllón, Filippo Ruffini et al.· Scientific Reports· 0 citations
Positron emission tomography (PET) provides critical metabolic information for oncological imaging, yet its use is constrained by radiation exposure, cost, and limited availability. Synthesizing PET-like images from computed tomography (CT) has been proposed as a way to approximate metabolic information; however, exi...
Francesco Di Feola, V. Guarrasi, Mikael Johansson et al.· Frontiers in Artificial Inte...· 0 citations
Cross-modality medical image translation can reduce the burden of multi-modal acquisitions, yet the field remains constrained by two coupled limitations: methods operate on 2D slices or 3D patches rather than whole volumes, and train a separate model for each translation task. Both stem from a single cause, the absence...
Daniele Molino, Alessio Zoboli, Camillo Maria Caruso et al.· 0 citations
This work presents a reproducible benchmark for 3D I2I translation in oncological imaging that compares seven generative models: three Generative Adversarial Networks (Pix2Pix, CycleGAN, and SRGAN) and four latent models (Latent Diffusion Model, Latent Diffusion Model+ControlNet, Brownian Bridge, and Flow Matching).
G. Romoli, A. Capoccia, Filippo Ruffini et al.· arXiv.org· 1 citation
This work proposes Source Anchoring for Physical Consistency (SAPC), a Functional Flow Matching method that encodes the physical constraints into the source noise before generation begins, and identifies the source distribution as a key design choice for physically consistent generative modelling.
Computed tomography (CT) and positron emission tomography (PET) provide complementary anatomical and functional information for cancer diagnosis and treatment planning. However, the widespread use of PET is limited by high radiation exposure, elevated costs, and restricted availability. To address these limitations, de...
Sarita Mourya, Francesco Di Feola, Pierangelo Veltri et al.· 0 citations
VegSim, a geospatial world model for scenario-conditioned vegetation simulation that infers a latent vegetation state from sparse satellite-derived NDVI histories, past meteorological covariates, and static spatial context, propagates it forward under future weather forcing through recurrent latent dynamics, and decode...
Irene Iele, Elena Mulero Ayllon, P. Soda et al.· arXiv.org· 0 citations
Future research should prioritize harmonized multicenter studies and biologically informed multimodal analytical frameworks to enable the routine implementation of microenvironment-oriented precision imaging in GBM management.
Lorenzo Arena, G. Espa, Giovanni Bertalot et al.· Frontiers in Medicine· 0 citations
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