82 citations · 307 across the 37 of their papers we have counts for
38 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…
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…
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…
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…
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…
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…
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…
Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises
Afifa Khaled, Mohammed Sabir, Rizwan Qureshi +4
The Medical Information Mart for Intensive Care (MIMIC) datasets have become the Kernel of Digital Health Research by providing freely accessible, deidentified records from tens of…
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…
Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging
Elena Mulero Ayllón, Massimiliano Mantegna, Linlin Shen +3
Accurate lung tumor segmentation is crucial for improving diagnosis, treatment planning, and patient outcomes in oncology. However, the complexity of tumor morphology, size, and lo…
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 the Generative Learning Trilemma: Generative Model Assessment in Data Scarcity Domains
Marco Salmè, Lorenzo Tronchin, Rosa Sicilia +2
Data scarcity remains a critical bottleneck impeding technological advancements across various domains, including but not limited to medicine and precision agriculture. To address…
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…
Texture-Aware StarGAN for CT data harmonisation
Francesco Di Feola, Ludovica Pompilio, Cecilia Assolito +2
Computed Tomography (CT) plays a pivotal role in medical diagnosis; however, variability across reconstruction kernels hinders data-driven approaches, such as deep learning models,…
Whole-Body Image-to-Image Translation for a Virtual Scanner in a Healthcare Digital Twin
Valerio Guarrasi, Francesco Di Feola, Rebecca Restivo +2
Generating positron emission tomography (PET) images from computed tomography (CT) scans via deep learning offers a promising pathway to reduce radiation exposure and costs associa…
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…