82 citations · 380 across the 53 of their papers we have counts for
58 papers
NoduleFlow: Automatic 3D Lung Nodule Detection and Segmentation
Giulia Romoli, Filippo Ruffini, Francesco Di Feola +3
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 identi…
Source Anchoring for Physical Consistency in Flow Matching Models
Giulia Romoli, Filippo Ruffini, Paolo Soda
Deep generative models are used to solve partial differential equations and model distributions of physical system states, but ensuring that the generated samples satisfy the gover…
3D CT-to-PET Translation via Latent Brownian Bridge Diffusion
Sarita Mourya, Francesco Di Feola, Pierangelo Veltri +1
Computed tomography (CT) and positron emission tomography (PET) provide complementary anatomical and functional information for cancer diagnosis and treatment planning. However, th…
Compositional Cross-Modality Translation via Whole-Volume Multitask Latent Flow Matching
Daniele Molino, Alessio Zoboli, Camillo Maria Caruso +2
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 slic…
Is Self-Pretraining really useful to improve diagnosis in medical Time Series?
Omar Coser, Antonio Orvieto, Paolo Soda +1
Inspired by recent evidence that transformer architectures benefit from Self-PreTraining (SPT) on long-context benchmarks, we investigate whether similar gains extend to multimodal…
SHOVIR: A Benchmark for Evaluating Vision Shortcut Learning in Radiology Report Generation
Filippo Ruffini, Marco Salmé, Rosa Sicilia +2
Current evaluation protocols for Vision-Language Models (VLMs) in Radiology Report Generation (RRG) rely on report-level metrics that measure lexical overlap or aggregate clinical…
VegSim: A Geospatial World Model for Scenario-Conditioned Vegetation Simulation
Irene Iele, Elena Mulero Ayllón, Paolo Soda +1
Vegetation monitoring under climate stress requires answering not only how it will evolve given the expected weather, but how it would respond to alternative meteorological conditi…
Cross Modality Image Translation In Medical Imaging Using Generative Frameworks
Giulia Romoli, Alessia Capoccia, Filippo Ruffini +20
Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) provide complementary information about tissues. Medical image-to-image (I2I) tra…
Resilient Vision-Tabular Multimodal Learning under Modality Missingness
Camillo Maria Caruso, Valerio Guarrasi, Paolo Soda
Multimodal deep learning has shown strong potential in medical applications by integrating heterogeneous data sources such as medical images and structured clinical variables. Howe…
Multimodal Stepwise Clinically-Guided Attention Learning for Pathological Complete Response Prediction in Breast Cancer
Alice Natalina Caragliano, Valerio Guarrasi, Michela Gravina +2
Pathological complete response (pCR) is a key prognostic factor in breast cancer patients undergoing neoadjuvant therapy, strongly associated with long-term survival and treatment…
Virtual Scanning for NSCLC Histology: Investigating the Discriminatory Power of Synthetic PET
Fatih Aksu, Laura Ciuffetti, Francesco Di Feola +6
Accurate histological differentiation between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) is critical for personalized treatment in non-small cell lung cancer (NSCLC). W…
Learning from Limited and Incomplete Data: A Multimodal Framework for Predicting Pathological Response in NSCLC
Alice Natalina Caragliano, Giulia Farina, Fatih Aksu +10
Major pathological response (pR) following neoadjuvant therapy is a clinically meaningful endpoint in non-small cell lung cancer, strongly associated with improved survival. Howeve…
A Systematic Benchmark of GAN Architectures for MRI-to-CT Synthesis
Alessandro Pesci, Valerio Guarrasi, Marco Alì +2
The translation from Magnetic resonance imaging (MRI) to Computed tomography (CT) has been proposed as an effective solution to facilitate MRI-only clinical workflows while limitin…
Longitudinal NSCLC Treatment Progression via Multimodal Generative Models
Massimiliano Mantegna, Elena Mulero Ayllón, Alice Natalina Caragliano +10
Predicting tumor evolution during radiotherapy is a clinically critical challenge, particularly when longitudinal changes are driven by both anatomy and treatment. In this work, we…
Retrieval-Augmented Anatomical Guidance for Text-to-CT Generation
Daniele Molino, Camillo Maria Caruso, Paolo Soda +1
Text-conditioned generative models for volumetric medical imaging provide semantic control but lack explicit anatomical guidance, often resulting in outputs that are spatially ambi…
Hybrid Quantum Neural Network for Multivariate Clinical Time Series Forecasting
Irene Iele, Floriano Caprio, Paolo Soda +1
Forecasting physiological signals can support proactive monitoring and timely clinical intervention by anticipating critical changes in patient status. In this work, we address mul…
Fairness-Aware Partial-label Domain Adaptation for Voice Classification of Parkinson's and ALS
Arianna Francesconi, Zhixiang Dai, Arthur Stefano Moscheni +7
Voice-based digital biomarkers can enable scalable, non-invasive screening and monitoring of Parkinson's disease (PD) and Amyotrophic Lateral Sclerosis (ALS). However, models train…
Concept-Enhanced Multimodal RAG: Towards Interpretable and Accurate Radiology Report Generation
Marco Salmè, Federico Siciliano, Fabrizio Silvestri +3
Radiology Report Generation (RRG) through Vision-Language Models (VLMs) promises to reduce documentation burden, improve reporting consistency, and accelerate clinical workflows. H…
Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates
Irene Iele, Giulia Romoli, Daniele Molino +4
Short-term forecasting of vegetation dynamics is a key enabler for data-driven decision support in precision agriculture. Normalized Difference Vegetation Index (NDVI) forecasting…
Handling Missing Modalities in Multimodal Survival Prediction for Non-Small Cell Lung Cancer
Filippo Ruffini, Camillo Maria Caruso, Claudia Tacconi +16
Accurate survival prediction in Non-Small Cell Lung Cancer (NSCLC) requires integrating clinical, radiological, and histopathological data. Multimodal Deep Learning (MDL) can impro…
Context-Gated Cross-Modal Perception with Visual Mamba for PET-CT Lung Tumor Segmentation
Elena Mulero Ayllón, Linlin Shen, Pierangelo Veltri +4
Accurate lung tumor segmentation is vital for improving diagnosis and treatment planning, and effectively combining anatomical and functional information from PET and CT remains a…
Cross-dataset Multivariate Time-series Model for Parkinson's Diagnosis via Keyboard Dynamics
Arianna Francesconi, Donato Cappetta, Fabio Rebecchi +3
Parkinson's disease (PD) presents a growing global challenge, affecting over 10 million individuals, with prevalence expected to double by 2040. Early diagnosis remains difficult d…
Sample-Aware Test-Time Adaptation for Medical Image-to-Image Translation
Irene Iele, Francesco Di Feola, Valerio Guarrasi +1
Image-to-image translation has emerged as a powerful technique in medical imaging, enabling tasks such as image denoising and cross-modality conversion. However, it suffers from li…
SPARSE Data, Rich Results: Few-Shot Semi-Supervised Learning via Class-Conditioned Image Translation
Guido Manni, Clemente Lauretti, Loredana Zollo +1
Deep learning has revolutionized medical imaging, but its effectiveness is severely limited by insufficient labeled training data. This paper introduces a novel GAN-based semi-supe…