82 citations · 380 across the 53 of their papers we have counts for
26 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
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…
Lesion-Aware Generative Artificial Intelligence for Virtual Contrast-Enhanced Mammography in Breast Cancer
Aurora Rofena, Arianna Manchia, Claudia Lucia Piccolo +3
Contrast-Enhanced Spectral Mammography (CESM) is a dual-energy mammographic technique that improves lesion visibility through the administration of an iodinated contrast agent. It…
Multimodal Doctor-in-the-Loop: A Clinically-Guided Explainable Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer
Alice Natalina Caragliano, Claudia Tacconi, Carlo Greco +7
This study proposes a novel approach combining Multimodal Deep Learning with intrinsic eXplainable Artificial Intelligence techniques to predict pathological response in non-small…
Evaluating Vision Language Model Adaptations for Radiology Report Generation in Low-Resource Languages
Marco Salmè, Rosa Sicilia, Paolo Soda +1
The integration of artificial intelligence in healthcare has opened new horizons for improving medical diagnostics and patient care. However, challenges persist in developing syste…
Any-to-Any Vision-Language Model for Multimodal X-ray Imaging and Radiological Report Generation
Daniele Molino, Francesco di Feola, Linlin Shen +2
Generative models have revolutionized Artificial Intelligence (AI), particularly in multimodal applications. However, adapting these models to the medical domain poses unique chall…
Beyond a Single Mode: GAN Ensembles for Diverse Medical Data Generation
Lorenzo Tronchin, Tommy Löfstedt, Paolo Soda +1
The advancement of generative AI, particularly in medical imaging, confronts the trilemma of ensuring high fidelity, diversity, and efficiency in synthetic data generation. While G…
Class Balancing Diversity Multimodal Ensemble for Alzheimer's Disease Diagnosis and Early Detection
Arianna Francesconi, Lazzaro di Biase, Donato Cappetta +4
Alzheimer's disease (AD) poses significant global health challenges due to its increasing prevalence and associated societal costs. Early detection and diagnosis of AD are critical…