Carlo Alberto Barbano

Multi-target Stain Normalization for Histology Slides

Integrating Prior Knowledge in Contrastive Learning with Kernel

Artificial intelligence methods for biomedical imaging and omics data

Fairness, Debiasing and Privacy in Computer Vision and Medical Imaging

The silence of the weights: a structural pruning strategy for attention-based audio signal architectures with second order metrics

Transformer-based models have become the state of the art across multiple domains, from natural language processing to machine listening, thanks to the attention mechanisms. However, the attention...

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

Cardiac Output (CO) is a key parameter in the diagnosis and management of cardiovascular diseases. However, its accurate measurement requires right-heart catheterization, an invasive and...

If you can describe it, they can see it: Cross-Modal Learning of Visual Concepts from Textual Descriptions

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 extend this ability...

Robust brain age estimation from structural MRI with contrastive learning

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 scalable and robust...

Unsupervised contrastive analysis for anomaly detection in brain MRIs via conditional diffusion models

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). In the context of...

Unsupervised Learning of Unbiased Visual Representations

Deep neural networks often struggle to learn robust representations in the presence of dataset biases, leading to suboptimal generalization on unbiased datasets. This limitation arises because the...

Integrating Prior Knowledge in Contrastive Learning with Kernel

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 representation. In...

Unbiased Supervised Contrastive Learning

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 distribution of the data....

Contrastive learning for regression in multi-site brain age prediction

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 find new...

Automated Prediction of Paravalvular Regurgitation before Transcatheter Aortic Valve Implantation

Severe aortic stenosis is a common and life-threatening condition in elderly patients, often treated with Transcatheter Aortic Valve Implantation (TAVI). Despite procedural advances, paravalvular...