6 papers
Novel Knowledge-Guided Generative Methods for Synthetic Transcriptomic Data
Francesca Pia Panaccione, Sofia Mongardi, Marco Masseroli +1
As biomedical research increasingly relies on data-intensive tools, the quality and utility of datasets are critical. Challenges such as imbalances, biases, and ethical or legal co…
Knowledge-Guided 3D CT Generation: A Conditioning-Centric Taxonomy
Francesca Pia Panaccione, Eugenio Lomurno, Matteo Matteucci
Controllable generation guided by external knowledge is a key requirement in modern generative deep learning applications, enabling the synthesis of samples with explicit constrain…
M-Gen: Interpretable Multimodal Generation of Gene Expression Profiles Using Clinical and Imaging Data
Francesca Pia Panaccione, Carlo Sgaravatti, Marco Venere
Integrating heterogeneous biomedical data, including clinical metadata, histopathology images, and molecular profiles, is crucial for comprehensive disease understanding. However,…
Evidence-Based Text-Conditioned 3D CT Synthesis for Ovarian Cancer
Francesca Pia Panaccione, Eugenio Lomurno, Francesca Fati +11
Ovarian cancer is frequently diagnosed at an advanced stage, making preoperative contrast-enhanced computed tomography (CT) central to staging and surgical planning; yet the scarci…
Inference-Time Refinement Closes the Synthetic-Real Gap in Tabular Diffusion
Eugenio Lomurno, Filippo Balzarini, Francesco Benelle +2
Diffusion-based generators set the current state of the art for synthetic tabular data. These methods approach but rarely exceed real-data utility, and closing this synthetic-real…
GeMM-GAN: A Multimodal Generative Model Conditioned on Histopathology Images and Clinical Descriptions for Gene Expression Profile Generation
Francesca Pia Panaccione, Carlo Sgaravatti, Pietro Pinoli
Biomedical research increasingly relies on integrating diverse data modalities, including gene expression profiles, medical images, and clinical metadata. While medical images and…