Arco Lab

ArCo Lab

ArCo at Università Campus Bio-Medico di Roma specializes in artificial intelligence research, with applications spanning medicine, industrial and environmental monitoring, energy management, and digital twins. Established in 2004, the lab develops AI-driven methodologies including multimodal learning, AI resilience, and computer vision, with a strong focus on oncology, connected health, medical records, and biomedical signals.

Research Areas

Research areas from intelligent methods to real-world translation

Artificial Intelligence

We develop AI methods for multimodal data, generative modelling, time-series analysis, and decision support, with attention to robustness, interpretability, and explainability.

Generative AI Multimodal AI Deep Learning

Computer Systems

We design and optimize computing systems that bring intelligence close to sensors and devices, including embedded platforms, IoT architectures, edge computing, and performance-aware AI deployment.

Embedded Systems Edge AI IoT

Control & Dynamical Systems

We develop control and estimation methods for networked and dynamical systems, including distributed filtering, consensus algorithms, nonlinear dynamics modelling, and adaptive biomedical control.

Networked Control Distributed Estimation Dynamical Systems

Applications

We apply AI and system-level methods across healthcare, industrial processes, agriculture, and data-intensive domains, focusing on reliability, interpretability, and measurable real-world impact.

Clinical AI Agriculture Industry 4.0

Translation

We support the translation of research into operational solutions, working with companies, institutions, and clinical partners to prototype, validate, and deploy data-driven technologies.

Technology Transfer Prototyping Validation

Active Research Projects

ADELAI

ADELAI

Alzheimer’s DiseasE Longitudinal study with Artificial Intelligence

2 collaborators 2025
Open project page
AIDA

AIDA

Explainable AI for integrating multimodal data in clinical applications

3 collaborators 2023-2026
Open project page
Cyber ACN

Cyber ACN

Sicurezza dei dati medici: strumenti di Intelligenza Artificiale Generativa per la condivisione e l'anonimizzazione sicura dei dati

4 collaborators
Open project page
IDEA

IDEA

AI-powered Digital Twin for next-generation lung cancer care

4 collaborators
Open project page
LUMINATE

LUMINATE

Advancing Lung Cancer Screening: Artificial Intelligence, Multimodal Imaging and Cutting-Edge Technologies for Early Detection and Characterization

4 collaborators
Open project page
PICTURE

PICTURE

Predicting pathological complete response after neoadjuvant therapies in NSCLC using multimodal data

2 collaborators 2023-2026
Open project page
Rome Technopole

Rome Technopole

Human-Centric Artificial Intelligence - Flagship Project 8

5 collaborators
Open project page
VirtualScanner

VirtualScanner

Intelligenza Aumentata per democratizzare la diagnosi di polmonite

2 collaborators
Open project page
XGeM+

XGeM+

XGeM+ - Generazione multimodale di immagini e referti medici

5 collaborators 2025
Open project page

Meet the Research Group

ArCo Lab is built on multidisciplinary collaboration among computer engineers, biomedical engineers, data scientists, postdocs, PhD students, researchers, and professors, combining methodological depth with clinical and applied perspectives.

Open Team Page
ArCo Lab team

Recent Publications

  1. Beyond a single mode: GAN ensembles for diverse medical data generation
    Lorenzo Tronchin, Tommy Löfstedt, Paolo Soda, and Valerio Guarrasi
    2026
    generative AI medical imaging
  2. [18F]FDG PET/CT Radiomics for Predicting Pathological Risk Subtypes of Thymic Epithelial Tumors: A Bicentric Study
    Antonio Sarubbi, Luca Frasca, Fatih Aksu, Guido Maria Meduri, Valerio Guarrasi, Gaetano Romano, Carmelina Cristina Zirafa, Filippo Longo, Gaetano Russo, Rosario Francesco Grasso, and 3 more authors
    2026
    radiomics
  3. Concept-Enhanced Multimodal RAG: Towards Interpretable and Accurate Radiology Report Generation
    Marco Salmè, Federico Siciliano, Fabrizio Silvestri, Paolo Soda, Rosa Sicilia, and Valerio Guarrasi
    arXiv preprint arXiv:2602.15650, 2026