1 citations · 1 across the 5 of their papers we have counts for
10 papers
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…
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…
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…
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…
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…
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…