Addressing Deep Learning Model Calibration Using Evidential Neural Networks and Uncertainty-Aware Training-Reference-Cited by-同舟云学术

Addressing Deep Learning Model Calibration Using Evidential Neural Networks and Uncertainty-Aware Training

Published:2023-04-18 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)
language:
Short-container-title:

Author:

Dawood Tareen¹,Chan Emily¹,Razavi Reza¹,King Andrew P.¹,Puyol-Antón Esther¹

Affiliation:

1. King's College,School of Biomedical Engineering & Imaging Sciences,London,UK

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10230311/10230322/10230515.pdf?arnumber=10230515

Reference21 articles.

1. Improving model calibration with accuracy versus uncertainty optimization;krishnan;Advances in neural information processing systems,2020

2. Soft calibration objectives for neural networks;karandikar;Advances in neural information processing systems,2021

3. Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation;yu;International Conference on Medical Image Computing and Computer-Assisted Intervention,2019

4. Uncertainty-aware training for cardiac resynchronisation therapy response prediction;dawood;Statistical Atlases and Computational Models of the Heart,2022

5. Revisiting the Evaluation of Uncertainty Estimation and Its Application to Explore Model Complexity-Uncertainty Trade-Off

Cited by 1 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. Trustworthy clinical AI solutions: A unified review of uncertainty quantification in Deep Learning models for medical image analysis;Artificial Intelligence in Medicine;2024-04