A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods-Reference-Cited by-同舟云学术

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A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods

Published:2024-10 Issue: Volume:97 Page:103223
ISSN:1361-8415
Container-title:Medical Image Analysis
language:en
Short-container-title:Medical Image Analysis

Author:

Huang Ling^ORCID,Ruan Su^ORCID,Xing Yucheng^ORCID,Feng Mengling^ORCID

Publisher

Elsevier BV

Reference350 articles.

1. BARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification;Abdar;Inform. Sci.,2021

2. Hercules: Deep hierarchical attentive multilevel fusion model with uncertainty quantification for medical image classification;Abdar;IEEE Trans. Ind. Inform.,2022

3. A review of uncertainty quantification in deep learning: Techniques, applications and challenges;Abdar;Inf. Fusion,2021

4. UncertaintyFuseNet: robust uncertainty-aware hierarchical feature fusion model with ensemble Monte Carlo dropout for COVID-19 detection;Abdar;Inf. Fusion,2023

5. Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning;Abdar;Comput. Biol. Med.,2021

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation;Information Fusion;2025-01

2. Evidential uncertainty sampling strategies for active learning;Machine Learning;2024-06-27

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