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 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
The need for quantification of uncertainty in artificial intelligence for clinical data analysis: increasing the level of trust in the decision-making process
Authors
Keywords
-
Journal
IEEE Systems Man and Cybernetics Magazine
Volume 8, Issue 3, Pages 28-40
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Online
2022-07-19
DOI
10.1109/msmc.2022.3150144
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach
- (2021) William Lotter et al. NATURE MEDICINE
- Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
- (2021) Eyke Hüllermeier et al. MACHINE LEARNING
- Show or suppress? Managing input uncertainty in machine learning model explanations
- (2021) Danding Wang et al. ARTIFICIAL INTELLIGENCE
- Uncertainty Quantification in Skin Cancer Classification using Three-Way Decision-based Bayesian Deep Learning
- (2021) Moloud Abdar et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Investigating the Prospect of Leveraging Blockchain and Machine Learning to Secure Vehicular Networks: A Survey
- (2021) Mahdi Dibaei et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- A review of uncertainty quantification in deep learning: Techniques, applications and challenges
- (2021) Moloud Abdar et al. Information Fusion
- BARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification
- (2021) Moloud Abdar et al. INFORMATION SCIENCES
- A survey on epistemic (model) uncertainty in supervised learning: Recent advances and applications
- (2021) Xinlei Zhou et al. NEUROCOMPUTING
- BrainIoT: Brain-Like Productive Services Provisioning With Federated Learning in Industrial IoT
- (2021) Hui Yang et al. IEEE Internet of Things Journal
- Recent advances in deep learning
- (2020) Xizhao Wang et al. International Journal of Machine Learning and Cybernetics
- Energy choices in Alaska: Mining people's perception and attitudes from geotagged tweets
- (2020) Moloud Abdar et al. RENEWABLE & SUSTAINABLE ENERGY REVIEWS
- A bibliometric analysis and cutting-edge overview on fuzzy techniques in Big Data
- (2020) Amit K. Shukla et al. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
- A novel method for sentiment classification of drug reviews using fusion of deep and machine learning techniques
- (2020) Mohammad Ehsan Basiri et al. KNOWLEDGE-BASED SYSTEMS
- Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
- (2020) Janni Yuval et al. Nature Communications
- Improving the accuracy of medical diagnosis with causal machine learning
- (2020) Jonathan G. Richens et al. Nature Communications
- Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells
- (2020) Brent M. Kuenzi et al. CANCER CELL
- Can deep learning algorithms outperform benchmark machine learning algorithms in flood susceptibility modeling?
- (2020) Binh Thai Pham et al. JOURNAL OF HYDROLOGY
- Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks
- (2020) Jiawen Yao et al. MEDICAL IMAGE ANALYSIS
- Model uncertainty, political contestation, and public trust in science: Evidence from the COVID-19 pandemic
- (2020) S. E. Kreps et al. Science Advances
- A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre
- (2020) Carol Y. Cheung et al. Nature Biomedical Engineering
- CAM: A fine-grained vehicle model recognition method based on visual attention model
- (2020) Ye Yu et al. IMAGE AND VISION COMPUTING
- Generative Adversarial Networks-Based Data Augmentation for Brain–Computer Interface
- (2020) Fatemeh Fahimi et al. IEEE Transactions on Neural Networks and Learning Systems
- Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles
- (2020) Szilard Aradi IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review
- (2020) Sajjad Mozaffari et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- RNN-K: A Reinforced Newton Method for Consensus-Based Distributed Optimization and Control Over Multiagent Systems
- (2020) Mou Wu et al. IEEE Transactions on Cybernetics
- Automated Segmentation of the Clinical Target Volume in the Planning CT for Breast Cancer Using Deep Neural Networks
- (2020) Xiaofeng Qi et al. IEEE Transactions on Cybernetics
- Continual lifelong learning with neural networks: A review
- (2019) German I. Parisi et al. NEURAL NETWORKS
- Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification
- (2019) Leyuan Fang et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Exploring uncertainty measures in deep networks for Multiple Sclerosis lesion detection and segmentation
- (2019) Tanya Nair et al. MEDICAL IMAGE ANALYSIS
- Generative adversarial network in medical imaging: A review
- (2019) Xin Yi et al. MEDICAL IMAGE ANALYSIS
- Artificial Intelligence in Medical Practice: The Question to the Answer?
- (2018) D. Douglas Miller et al. AMERICAN JOURNAL OF MEDICINE
- Recent Trends in Deep Learning Based Natural Language Processing [Review Article]
- (2018) Tom Young et al. IEEE Computational Intelligence Magazine
- Neural Network-Based Uncertainty Quantification: A Survey of Methodologies and Applications
- (2018) H. M. Dipu Kabir et al. IEEE Access
- Dynamic L-RNN recovery of missing data in IoMT applications
- (2018) Hamza Turabieh et al. Future Generation Computer Systems-The International Journal of eScience
- Harmony search algorithm for energy system applications: an updated review and analysis
- (2018) Morteza Nazari-Heris et al. JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE
- Explanation in artificial intelligence: Insights from the social sciences
- (2018) Tim Miller ARTIFICIAL INTELLIGENCE
- Parallel Processing Systems for Big Data: A Survey
- (2016) Yunquan Zhang et al. PROCEEDINGS OF THE IEEE
- The global burden of diagnostic errors in primary care
- (2016) Hardeep Singh et al. BMJ Quality & Safety
- The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations
- (2014) Hardeep Singh et al. BMJ Quality & Safety
- The incidence of diagnostic error in medicine
- (2013) Mark L Graber BMJ Quality & Safety
- Types and Origins of Diagnostic Errors in Primary Care Settings
- (2013) Hardeep Singh et al. JAMA Internal Medicine
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now