82 citations · 109 across the 10 of their papers we have counts for
12 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…
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…
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…
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…
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…
Benchmarking Foundation Models and Parameter-Efficient Fine-Tuning for Prognosis Prediction in Medical Imaging
Filippo Ruffini, Elena Mulero Ayllon, Linlin Shen +2
Despite the significant potential of Foundation Models (FMs) in medical imaging, their application to prognosis prediction remains challenging due to data scarcity, class imbalance…
From Alignment to Synthesis: Contrastive Volumetric Grounding for Text-to-CT Generation
Daniele Molino, Camillo Maria Caruso, Filippo Ruffini +2
Generating semantically controllable 3D CT volumes from radiology reports requires more than a rich text encoder, it requires vision-language alignment grounded in volumetric space…
Doctor-in-the-Loop: An Explainable, Multi-View Deep Learning Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer
Alice Natalina Caragliano, Filippo Ruffini, Carlo Greco +8
Non-small cell lung cancer (NSCLC) remains a major global health challenge, with high post-surgical recurrence rates underscoring the need for accurate pathological response predic…
A Systematic Review of Intermediate Fusion in Multimodal Deep Learning for Biomedical Applications
Valerio Guarrasi, Fatih Aksu, Camillo Maria Caruso +4
Deep learning has revolutionized biomedical research by providing sophisticated methods to handle complex, high-dimensional data. Multimodal deep learning (MDL) further enhances th…
Multi-Dataset Multi-Task Learning for COVID-19 Prognosis
Filippo Ruffini, Lorenzo Tronchin, Zhuoru Wu +4
In the fight against the COVID-19 pandemic, leveraging artificial intelligence to predict disease outcomes from chest radiographic images represents a significant scientific aim. T…