Carlo Alberto Barbano

Anatomical Foundation Models for Brain MRIs

Deep Learning (DL) in neuroimaging has become increasingly relevant for detecting neurological conditions and neurodegenerative disorders. One of the most predominant biomarkers in neuroimaging is...

Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray

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 predict coronary...

Say My Name: a Model's Bias Discovery Framework

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 to specific,...

EnD: Entangling and Disentangling deep representations for bias correction

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, like the...

Multi-target stain normalization for histology slides

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 diverse staining...

AI-Assisted Diagnosis for Covid-19 CXR Screening: From Data Collection to Clinical Validation

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 state-of-the-art...

Stacked Regression using Off-the-shelf, Stimulus-tuned and Fine-tuned Neural Networks for Predicting fMRI Brain Responses to Movies (Algonauts 2025 Report)

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 from large...

Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data

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 community. In this...

UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading

Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma....

A two-step explainable approach for COVID-19 computer-aided diagnosis from chest x-ray images

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 the most viable...

A Pair-Weighing Strategy for Enhancing Clip Zero-Shot Classification for Chest X-Rays

AI-Assisted Diagnosis for Covid-19 CXR Screening: from Data Collection to Clinical Validation

Automated Prediction of Paravalvular Regurgitation Before Transcatheter Aortic Valve Implantation

Cardiac Output Prediction From Echocardiograms: Self-Supervised Learning with Limited Data

Contrastive Learning for Regression in Multi-Site Brain Age Prediction

Bridging the gap between debiasing and privacy for deep learning

Unbiased Supervised Contrastive Learning

Improving Generative Data Augmentation with Prior-Knowledge for Dysplasia Grading of Colorectal Polyps