1 citations · 2 across the 12 of their papers we have counts for
14 papers
Knowledge-Guided 3D CT Generation: A Conditioning-Centric Taxonomy
Francesca Pia Panaccione, Eugenio Lomurno, Matteo Matteucci
Controllable generation guided by external knowledge is a key requirement in modern generative deep learning applications, enabling the synthesis of samples with explicit constrain…
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…
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…
Inference-Time Refinement Closes the Synthetic-Real Gap in Tabular Diffusion
Eugenio Lomurno, Filippo Balzarini, Francesco Benelle +2
Diffusion-based generators set the current state of the art for synthetic tabular data. These methods approach but rarely exceed real-data utility, and closing this synthetic-real…
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…
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…
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…
POMONAG: Pareto-Optimal Many-Objective Neural Architecture Generator
Eugenio Lomurno, Samuele Mariani, Matteo Monti +1
Neural Architecture Search (NAS) automates neural network design, reducing dependence on human expertise. While NAS methods are computationally intensive and dataset-specific, auxi…
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…
A Lightweight Neural Architecture Search Model for Medical Image Classification
Lunchen Xie, Eugenio Lomurno, Matteo Gambella +4
Accurate classification of medical images is essential for modern diagnostics. Deep learning advancements led clinicians to increasingly use sophisticated models to make faster and…
Stable Diffusion Dataset Generation for Downstream Classification Tasks
Eugenio Lomurno, Matteo D'Oria, Matteo Matteucci
Recent advances in generative artificial intelligence have enabled the creation of high-quality synthetic data that closely mimics real-world data. This paper explores the adaptati…
Harnessing the Computing Continuum across Personalized Healthcare, Maintenance and Inspection, and Farming 4.0
Fatemeh Baghdadi, Davide Cirillo, Daniele Lezzi +6
The AI-SPRINT project, launched in 2021 and funded by the European Commission, focuses on the development and implementation of AI applications across the computing continuum. This…
Age Group Discrimination via Free Handwriting Indicators
Eugenio Lomurno, Simone Toffoli, Davide Di Febbo +3
The growing global elderly population is expected to increase the prevalence of frailty, posing significant challenges to healthcare systems. Frailty, a syndrome associated with ag…
Neural Architecture Transfer 2: A Paradigm for Improving Efficiency in Multi-Objective Neural Architecture Search
Simone Sarti, Eugenio Lomurno, Matteo Matteucci
Deep learning is increasingly impacting various aspects of contemporary society. Artificial neural networks have emerged as the dominant models for solving an expanding range of ta…