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

• Published in: Computational statistics & data analysis Vol. 142; p. 106816
• Main Authors: Kwon, Yongchan, Won, Joong-Ho, Kim, Beom Joon, Paik, Myunghee Cho
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2020
• Subjects: Aleatoric and epistemic uncertainty; algorithms; Bayesian neural network; Bayesian theory; computer vision; data collection; Ischemic stroke lesion segmentation; neural networks; prediction; Retinal blood vessel segmentation; stroke; uncertainty; Uncertainty quantification; Uncertainty quantification; Retinal blood vessel segmentation; Ischemic stroke lesion segmentation; Bayesian neural network; Aleatoric and epistemic uncertainty

Most recent research of deep neural networks in the field of computer vision has focused on improving performances of point predictions by developing network architectures or learning algorithms. Reliable uncertainty quantification accompanied by point estimation can lead to a more informed decision...