Enabling uncertainty estimation in neural networks through weight perturbation for improved Alzheimer's disease classification.

ABSTRACT:

Background

The willingness to trust predictions formulated by automatic algorithms is key in a wide range of domains. However, a vast number of deep architectures are only able to formulate predictions without associated uncertainty.

Purpose

In this study, we propose a method to convert a standard neural network into a Bayesian neural network and estimate the variability of predictions by sampling different networks similar to the original one at each forward pass.

Methods

We combine our method with a tunable rejection-based approach that employs only the fraction of the data, i.e., the share that the model can classify with an uncertainty below a user-set threshold. We test our model in a large cohort of brain images from patients with Alzheimer's disease and healthy

SUBMITTER: Ferrante M 

PROVIDER: S-EPMC10876844 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature