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cs.CV2026

Evidence-Based Text-Conditioned 3D CT Synthesis for Ovarian Cancer

Francesca Pia Panaccione, Eugenio Lomurno, Francesca Fati +11

Ovarian cancer is frequently diagnosed at an advanced stage, making preoperative contrast-enhanced computed tomography (CT) central to staging and surgical planning; yet the scarci…

cs.CV2026

A Structured Benchmark for Text-Guided Anomaly Detection: When Language Stops Conditioning the Decision

Stefano Samele, Eugenio Lomurno, Teodora Jovanovic +3

Industrial anomaly detection has historically been a unimodal task. Recent multimodal vision-language models have produced systems that admit textual input alongside the image and…

cs.CV2025

Your Image Generator Is Your New Private Dataset

Nicolo Resmini, Eugenio Lomurno, Cristian Sbrolli +1

Generative diffusion models have emerged as powerful tools to synthetically produce training data, offering potential solutions to data scarcity and reducing labelling costs for do…

cs.CV2025

Neuro-Symbolic Scene Graph Conditioning for Synthetic Image Dataset Generation

Giacomo Savazzi, Eugenio Lomurno, Cristian Sbrolli +2

As machine learning models increase in scale and complexity, obtaining sufficient training data has become a critical bottleneck due to acquisition costs, privacy constraints, and…

cs.CV2025

ZO-DARTS++: An Efficient and Size-Variable Zeroth-Order Neural Architecture Search Algorithm

Lunchen Xie, Eugenio Lomurno, Matteo Gambella +4

Differentiable Neural Architecture Search (NAS) provides a promising avenue for automating the complex design of deep learning (DL) models. However, current differentiable NAS meth…

cs.CV2024

Synthetic Image Learning: Preserving Performance and Preventing Membership Inference Attacks

Eugenio Lomurno, Matteo Matteucci

Generative artificial intelligence has transformed the generation of synthetic data, providing innovative solutions to challenges like data scarcity and privacy, which are particul…