82 citations · 380 across the 53 of their papers we have counts for
20 papers · 1 filter
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…
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…
Deep Learning for Human Locomotion Analysis in Lower-Limb Exoskeletons: A Comparative Study
Omar Coser, Christian Tamantini, Matteo Tortora +4
Wearable robotics for lower-limb assistance have become a pivotal area of research, aiming to enhance mobility for individuals with physical impairments or augment the performance…
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…
Augmented Intelligence for Multimodal Virtual Biopsy in Breast Cancer Using Generative Artificial Intelligence
Aurora Rofena, Claudia Lucia Piccolo, Bruno Beomonte Zobel +2
Full-Field Digital Mammography (FFDM) is the primary imaging modality for routine breast cancer screening; however, its effectiveness is limited in patients with dense breast tissu…
Multi-stage intermediate fusion for multimodal learning to classify non-small cell lung cancer subtypes from CT and PET
Fatih Aksu, Fabrizia Gelardi, Arturo Chiti +1
Accurate classification of histological subtypes of non-small cell lung cancer (NSCLC) is essential in the era of precision medicine, yet current invasive techniques are not always…
XGeM: A Multi-Prompt Foundation Model for Multimodal Medical Data Generation
Daniele Molino, Francesco Di Feola, Eliodoro Faiella +5
The adoption of Artificial Intelligence in medical imaging holds great promise, yet it remains hindered by challenges such as data scarcity, privacy concerns, and the need for robu…