Objective evaluation of deep uncertainty predictions for covid-19 detection
Deep neural networks (DNNs) have been widely applied for detecting COVID-19 in medical
images. Existing studies mainly apply transfer learning and other data representation …
images. Existing studies mainly apply transfer learning and other data representation …
An uncertainty-aware transfer learning-based framework for COVID-19 diagnosis
A Shamsi, H Asgharnezhad… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
The early and reliable detection of COVID-19 infected patients is essential to prevent and
limit its outbreak. The PCR tests for COVID-19 detection are not available in many countries …
limit its outbreak. The PCR tests for COVID-19 detection are not available in many countries …
Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection
Deep Learning has achieved state of the art performance in medical imaging. However,
these methods for disease detection focus exclusively on improving the accuracy of …
these methods for disease detection focus exclusively on improving the accuracy of …
Improving uncertainty estimation with semi-supervised deep learning for COVID-19 detection using chest X-ray images
In this work we implement a COVID-19 infection detection system based on chest X-ray
images with uncertainty estimation. Uncertainty estimation is vital for safe usage of computer …
images with uncertainty estimation. Uncertainty estimation is vital for safe usage of computer …
Uncertainty-aware convolutional neural network for COVID-19 X-ray images classification
Deep learning (DL) has shown great success in the field of medical image analysis. In the
wake of the current pandemic situation of SARS-CoV-2, a few pioneering works based on …
wake of the current pandemic situation of SARS-CoV-2, a few pioneering works based on …
UncertaintyFuseNet: robust uncertainty-aware hierarchical feature fusion model with ensemble Monte Carlo dropout for COVID-19 detection
Abstract The COVID-19 (Coronavirus disease 2019) pandemic has become a major global
threat to human health and well-being. Thus, the development of computer-aided detection …
threat to human health and well-being. Thus, the development of computer-aided detection …
Explainable artificial intelligence-based edge fuzzy images for COVID-19 detection and identification
Q Hu, FNB Gois, R Costa, L Zhang, L Yin… - Applied Soft …, 2022 - Elsevier
The COVID-19 pandemic continues to wreak havoc on the world's population's health and
well-being. Successful screening of infected patients is a critical step in the fight against it …
well-being. Successful screening of infected patients is a critical step in the fight against it …
Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images
Chest X-ray images are used in deep convolutional neural networks for the detection of
COVID-19, the greatest human challenge of the 21st century. Robustness to noise and …
COVID-19, the greatest human challenge of the 21st century. Robustness to noise and …
RCoNet: Deformable mutual information maximization and high-order uncertainty-aware learning for robust COVID-19 detection
The novel 2019 Coronavirus (COVID-19) infection has spread worldwide and is currently a
major healthcare challenge around the world. Chest computed tomography (CT) and X-ray …
major healthcare challenge around the world. Chest computed tomography (CT) and X-ray …
The effect of machine learning explanations on user trust for automated diagnosis of COVID-19
Recent years have seen deep neural networks (DNN) gain widespread acceptance for a
range of computer vision tasks that include medical imaging. Motivated by their …
range of computer vision tasks that include medical imaging. Motivated by their …