The need for quantification of uncertainty in artificial intelligence for clinical data analysis: increasing the level of trust in the decision-making process-Reference-Cited by-同舟云学术

The need for quantification of uncertainty in artificial intelligence for clinical data analysis: increasing the level of trust in the decision-making process

Published:2022-07 Issue:3 Volume:8 Page:28-40
ISSN:2333-942X
Container-title:IEEE Systems, Man, and Cybernetics Magazine
language:
Short-container-title:IEEE Syst. Man Cybern. Mag.

Author:

Abdar Moloud¹,Khosravi Abbas¹,Islam Sheikh Mohammed Shariful²,Acharya U. Rajendra³,Vasilakos Athanasios V.⁴

Affiliation:

1. Institute for Intelligent Systems Research and Innovations, Deakin University, Geelong, Australia

2. School of Exercise and Nutrition Sciences, Deakin University, Melbourne, Australia

3. Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan

4. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China

Funder

Australian Research Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Human-Computer Interaction,Human Factors and Ergonomics,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/6745853/9831792/09831802.pdf?arnumber=9831802

Reference96 articles.

1. Continual lifelong learning with neural networks: A review

2. Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks

3. Channel Attention Module with Multi-scale Grid Average Pooling for Breast Cancer Segmentation in an Ultrasound Image

4. Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification

5. Uncertainty-guided continual learning in Bayesian neural networks—Extended abstract;ebrahimi;Proc IEEE Conf Comput Vis Pattern Recognition (CVPR),0

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