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[HTML][HTML] Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis
The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …
respect to the quantity of high-performing solutions reported in the literature. End users are …
Explainable deep convolutional neural networks for insect pest recognition
Fungal infestation of crops is critical to food security as it affects yield and quality of
production. Indeed, one element responsible for this situation is insect pests. Early detection …
production. Indeed, one element responsible for this situation is insect pests. Early detection …
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 …
[HTML][HTML] Uncertainty estimation in medical image classification: systematic review
A Kurz, K Hauser, HA Mehrtens… - JMIR Medical …, 2022 - medinform.jmir.org
Background: Deep neural networks are showing impressive results in different medical
image classification tasks. However, for real-world applications, there is a need to estimate …
image classification tasks. However, for real-world applications, there is a need to estimate …
Artificial intelligence bias in medical system designs: a systematic review
Inherent bias in the artificial intelligence (AI)-model brings inaccuracies and variabilities
during clinical deployment of the model. It is challenging to recognize the source of bias in AI …
during clinical deployment of the model. It is challenging to recognize the source of bias in AI …
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 …
[HTML][HTML] Covid-19 detection in ct/x-ray imagery using vision transformers
The steady spread of the 2019 Coronavirus disease has brought about human and
economic losses, imposing a new lifestyle across the world. On this point, medical imaging …
economic losses, imposing a new lifestyle across the world. On this point, medical imaging …
Image enhancement techniques on deep learning approaches for automated diagnosis of COVID-19 features using CXR images
The outbreak of novel coronavirus (COVID-19) disease has infected more than 135.6 million
people globally. For its early diagnosis, researchers consider chest X-ray examinations as a …
people globally. For its early diagnosis, researchers consider chest X-ray examinations as a …
Detection of SARS-CoV-2 virus using lightweight convolutional neural networks
A Kumar, BK Chaurasia - Wireless Personal Communications, 2024 - Springer
A highly contagious illness caused by the SARS-CoV-2 virus pandemic is proven to wreak
havoc on people's health and well-being all over the globe. Severe Acute Respiratory …
havoc on people's health and well-being all over the globe. Severe Acute Respiratory …
[HTML][HTML] Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning
Abstract Coronavirus disease (COVID-19) has caused a worldwide pandemic, putting
millions of people's health and lives in jeopardy. Detecting infected patients early on chest …
millions of people's health and lives in jeopardy. Detecting infected patients early on chest …