15 citations · 30 across the 8 of their papers we have counts for
6 papers · 1 filter
Fixing confirmation bias in feature attribution methods via semantic match
Giovanni Cinà, Daniel Fernandez-Llaneza, Ludovico Deponte +6
Feature attribution methods have become a staple method to disentangle the complex behavior of black box models. Despite their success, some scholars have argued that such methods…
Out-of-Distribution Detection for Medical Applications: Guidelines for Practical Evaluation
Karina Zadorozhny, Patrick Thoral, Paul Elbers +1
Detection of Out-of-Distribution (OOD) samples in real time is a crucial safety check for deployment of machine learning models in the medical field. Despite a growing number of un…
A pragmatic approach to estimating average treatment effects from EHR data: the effect of prone positioning on mechanically ventilated COVID-19 patients
Adam Izdebski, Patrick J. Thoral, Robbert C. A. Lalisang +43
Despite the recent progress in the field of causal inference, to date there is no agreed upon methodology to glean treatment effect estimation from observational data. The conseque…
Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD Detection
Dennis Ulmer, Giovanni Cinà
A crucial requirement for reliable deployment of deep learning models for safety-critical applications is the ability to identify out-of-distribution (OOD) data points, samples whi…
Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD Detection On Medical Tabular Data
Dennis Ulmer, Lotta Meijerink, Giovanni Cinà
When deploying machine learning models in high-stakes real-world environments such as health care, it is crucial to accurately assess the uncertainty concerning a model's predictio…
Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applications
David Ruhe, Giovanni Cinà, Michele Tonutti +2
The Intensive Care Unit (ICU) is a hospital department where machine learning has the potential to provide valuable assistance in clinical decision making. Classical machine learni…