Low dose Computed Tomography (CT) is the standard imaging modality for lung cancer screening. On CT scans, lung cancer typically manifests as pulmonary nodules, whose manual identification is time-consuming and prone to observer variability, image quality limitations, and suboptimal reading conditions. Moreover, subtle...
G. Romoli, Filippo Ruffini, Francesco Di Feola et al.· 0 citations
The robustness analysis reveals a trade-off: MedSAM tolerates prompt perturbations, while Medical SAM 2 achieves higher accuracy but degrades under box tightening and spatial shifts, supporting the use of SAM-based models for multi-cancer segmentation, while showing that reliability depends on prompt quality and anatom...
M. Tortora, Elena Mulero Ayllón, Filippo Ruffini et al.· Scientific Reports· 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.
G. Romoli, Filippo Ruffini, P. Soda· 0 citations
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