An exploration of uncertainty information for segmentation quality assessment-Reference-Cited by-同舟云学术

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An exploration of uncertainty information for segmentation quality assessment

Published:2020-03-10 Issue: Volume: Page:
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Container-title:Medical Imaging 2020: Image Processing
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Author:

Hoebel Katharina,Andrearczyk Vincent,Beers Andrew L.,Patel Jay B.,Chang Ken,Depeursinge Adrien,Mueller Henning,Kalpathy-Cramer Jayashree

Publisher

SPIE

Cited by 22 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. Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning;Communications Medicine;2024-06-08

2. Trustworthy clinical AI solutions: A unified review of uncertainty quantification in Deep Learning models for medical image analysis;Artificial Intelligence in Medicine;2024-04

3. Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

4. Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023);Computers in Biology and Medicine;2023-10

5. Fuzzy Uncertainty-Based Out-of-Distribution Detection Algorithm for Semantic Segmentation;2023 IEEE International Conference on Fuzzy Systems (FUZZ);2023-08-13

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