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

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

BackgroundThe 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.PurposeIn this study, we propose a method to convert a standard neur...

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Bibliographic Details
Main Authors: Matteo Ferrante, Tommaso Boccato, Nicola Toschi
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-02-01
Series:Frontiers in Neuroinformatics
Subjects:
Bayesian
uncertainty
deep learning
convolutional neural network (CNN)
classification
Online Access:https://www.frontiersin.org/articles/10.3389/fninf.2024.1346723/full

Internet

https://www.frontiersin.org/articles/10.3389/fninf.2024.1346723/full

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