The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …

Background: Deep neural networks are showing impressive results in different medical
image classification tasks. However, for real-world applications, there is a need to estimate …

Test-time adaptation provides an effective means of tackling the potential distribution shift
between model training and inference, by dynamically updating the model at test time. This …

When deployed for risk-sensitive tasks, deep neural networks must be able to detect
instances with labels from outside the distribution for which they were trained. In this paper …

Artificial intelligence (AI) solutions that automatically extract information from digital histology
images have shown great promise for improving pathological diagnosis. Prior to routine use …

Deep neural networks have been increasingly proposed for automated screening and
diagnosis of retinal diseases from optical coherence tomography (OCT), but often provide …

There has been a massive increase in research interest towards applying data driven
methods to problems in mechanics, with a particular emphasis on using data driven …

Estimating the uncertainty of a neural network plays a fundamental role in safety-critical
settings. In perception for autonomous driving, measuring the uncertainty means providing …

Identification and appropriate handling of inconsistencies in data at deployment time is
crucial to reliably use machine learning models. While recent data-centric methods are able …

An important limitation of state-of-the-art deep learning networks is that they do not
recognize when their input is dissimilar to the data on which they were trained and proceed …