221 citations · 296 across the 19 of their papers we have counts for
20 papers
Flattening the Connectome Spectrum: A Spectral Filter for FC Induces a Pretraining Target for fMRI Encoders
Giovanni Marraffini, Victoria Shevchenko, Carlo Alberto Barbano +1
Self-supervised pretraining reshaped prediction in language and vision, and brain foundation models (BFMs) inherited its promise. Representations learned from large unlabelled corp…
Cardiac Output Prediction from Echocardiograms: Self-Supervised Learning with Limited Data
Adson Duarte, Davide Vitturini, Emanuele Milillo +9
Cardiac Output (CO) is a key parameter in the diagnosis and management of cardiovascular diseases. However, its accurate measurement requires right-heart catheterization, an invasi…
Automated Prediction of Paravalvular Regurgitation before Transcatheter Aortic Valve Implantation
Michele Cannito, Riccardo Renzulli, Adson Duarte +9
Severe aortic stenosis is a common and life-threatening condition in elderly patients, often treated with Transcatheter Aortic Valve Implantation (TAVI). Despite procedural advance…
Stacked Regression using Off-the-shelf, Stimulus-tuned and Fine-tuned Neural Networks for Predicting fMRI Brain Responses to Movies (Algonauts 2025 Report)
Robert Scholz, Kunal Bagga, Christine Ahrends +1
We present our submission to the Algonauts 2025 Challenge, where the goal is to predict fMRI brain responses to movie stimuli. Our approach integrates multimodal representations fr…
Robust brain age estimation from structural MRI with contrastive learning
Carlo Alberto Barbano, Benoit Dufumier, Edouard Duchesnay +2
Estimating brain age from structural MRI has emerged as a powerful tool for characterizing normative and pathological aging. In this work, we explore contrastive learning as a scal…
If you can describe it, they can see it: Cross-Modal Learning of Visual Concepts from Textual Descriptions
Carlo Alberto Barbano, Luca Molinaro, Massimiliano Ciranni +3
Humans can visualize new and unknown concepts from their natural language description, based on their experience and previous knowledge. Insipired by this, we present a way to exte…
Say My Name: a Model's Bias Discovery Framework
Massimiliano Ciranni, Luca Molinaro, Carlo Alberto Barbano +4
In the last few years, due to the broad applicability of deep learning to downstream tasks and end-to-end training capabilities, increasingly more concerns about potential biases t…
Anatomical Foundation Models for Brain MRIs
Carlo Alberto Barbano, Matteo Brunello, Benoit Dufumier +1
Deep Learning (DL) in neuroimaging has become increasingly relevant for detecting neurological conditions and neurodegenerative disorders. One of the most predominant biomarkers in…
Multi-target stain normalization for histology slides
Desislav Ivanov, Carlo Alberto Barbano, Marco Grangetto
Traditional staining normalization approaches, e.g. Macenko, typically rely on the choice of a single representative reference image, which may not adequately account for the diver…
Unsupervised contrastive analysis for anomaly detection in brain MRIs via conditional diffusion models
Cristiano Patrício, Carlo Alberto Barbano, Attilio Fiandrotti +4
Contrastive Analysis (CA) detects anomalies by contrasting patterns unique to a target group (e.g., unhealthy subjects) from those in a background group (e.g., healthy subjects). I…
AI-Assisted Diagnosis for Covid-19 CXR Screening: From Data Collection to Clinical Validation
Carlo Alberto Barbano, Riccardo Renzulli, Marco Grosso +3
In this paper, we present the major results from the Covid Radiographic imaging System based on AI (Co.R.S.A.) project, which took place in Italy. This project aims to develop a st…
Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray
Guglielmo Gallone, Francesco Iodice, Alberto Presta +16
Aims. To develop a deep-learning based system for recognition of subclinical atherosclerosis on a plain frontal chest x-ray. Methods and Results. A deep-learning algorithm to predi…
Contrastive learning for regression in multi-site brain age prediction
Carlo Alberto Barbano, Benoit Dufumier, Edouard Duchesnay +2
Building accurate Deep Learning (DL) models for brain age prediction is a very relevant topic in neuroimaging, as it could help better understand neurodegenerative disorders and fi…
Unbiased Supervised Contrastive Learning
Carlo Alberto Barbano, Benoit Dufumier, Enzo Tartaglione +2
Many datasets are biased, namely they contain easy-to-learn features that are highly correlated with the target class only in the dataset but not in the true underlying distributio…
Integrating Prior Knowledge in Contrastive Learning with Kernel
Benoit Dufumier, Carlo Alberto Barbano, Robin Louiset +2
Data augmentation is a crucial component in unsupervised contrastive learning (CL). It determines how positive samples are defined and, ultimately, the quality of the learned repre…
Unsupervised Learning of Unbiased Visual Representations
Carlo Alberto Barbano, Enzo Tartaglione, Marco Grangetto
Deep neural networks often struggle to learn robust representations in the presence of dataset biases, leading to suboptimal generalization on unbiased datasets. This limitation ar…
EnD: Entangling and Disentangling deep representations for bias correction
Enzo Tartaglione, Carlo Alberto Barbano, Marco Grangetto
Artificial neural networks perform state-of-the-art in an ever-growing number of tasks, and nowadays they are used to solve an incredibly large variety of tasks. There are problems…
UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading
Carlo Alberto Barbano, Daniele Perlo, Enzo Tartaglione +4
Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carc…
A two-step explainable approach for COVID-19 computer-aided diagnosis from chest x-ray images
Carlo Alberto Barbano, Enzo Tartaglione, Claudio Berzovini +2
Early screening of patients is a critical issue in order to assess immediate and fast responses against the spread of COVID-19. The use of nasopharyngeal swabs has been considered…
Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
Enzo Tartaglione, Carlo Alberto Barbano, Claudio Berzovini +2
The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI commun…