Uncertainty quantification using Bayesian neural networks in classification: application to biomedical image segmentation

DOI10.1016/j.csda.2019.106816OpenAlexW2965563166MaRDI QIDQ2008102

Joong-Ho Won, Myunghee Cho Paik, Beom Joon Kim, Yongchan Kwon

Publication date: 22 November 2019

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2019.106816

zbMATH Keywords

uncertainty quantificationBayesian neural networkaleatoric and epistemic uncertaintyischemic stroke lesion segmentationretinal blood vessel segmentation

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Artificial neural networks and deep learning (68T07) Biomedical imaging and signal processing (92C55)

Related Items (1)

Numerical Analysis for Convergence of a Sample-Wise Backpropagation Method for Training Stochastic Neural Networks

Uses Software

• U-Net

Cites Work

• Statistical Methods for Rates and Proportions

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