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COVID-CT-Dataset: A CT Scan Dataset about COVID-19
CT scans are promising in providing accurate, fast, and cheap screening ...
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Uncertainty-Aware Semi-supervised Method using Large Unlabelled and Limited Labeled COVID-19 Data
The new coronavirus has caused more than 1 million deaths and continues ...
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Improving Automated COVID-19 Grading with Convolutional Neural Networks in Computed Tomography Scans: An Ablation Study
Amidst the ongoing pandemic, several studies have shown that COVID-19 cl...
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Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging
Our motivating application is a real-world problem: COVID-19 classificat...
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An Attention Mechanism with Multiple Knowledge Sources for COVID-19 Detection from CT Images
Until now, Coronavirus SARS-CoV-2 has caused more than 850,000 deaths an...
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Do not repeat these mistakes – a critical appraisal of applications of explainable artificial intelligence for image based COVID-19 detection
The sudden outbreak and uncontrolled spread of COVID-19 disease is one o...
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Localization and classification of intracranialhemorrhages in CT data
Intracranial hemorrhages (ICHs) are life-threatening brain injures with ...
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Covid-19 classification with deep neural network and belief functions
Computed tomography (CT) image provides useful information for radiologists to diagnose Covid-19. However, visual analysis of CT scans is time-consuming. Thus, it is necessary to develop algorithms for automatic Covid-19 detection from CT images. In this paper, we propose a belief function-based convolutional neural network with semi-supervised training to detect Covid-19 cases. Our method first extracts deep features, maps them into belief degree maps and makes the final classification decision. Our results are more reliable and explainable than those of traditional deep learning-based classification models. Experimental results show that our approach is able to achieve a good performance with an accuracy of 0.81, an F1 of 0.812 and an AUC of 0.875.
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