Exploiting probability density function of deep convolutional autoencoders’ latent space for reliable COVID-19 detection on CT scans

We present a probabilistic method for classifying chest computed tomography (CT) scans into COVID-19 and non-COVID-19. To this end, we design and train, in an unsupervised manner, a deep convolutional autoencoder (DCAE) on a selected training data set, which is composed only of COVID-19 CT scans. On...

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Vydané v:	The Journal of supercomputing Ročník 78; číslo 9; s. 12024 - 12045
Hlavní autori:	Sarv Ahrabi, Sima, Piazzo, Lorenzo, Momenzadeh, Alireza, Scarpiniti, Michele, Baccarelli, Enzo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.06.2022 Springer Nature B.V
Predmet:	Coders Compilers Computed tomography Computer Science Coronaviruses COVID-19 Datasets Interpreters Medical imaging Probabilistic methods Probability density functions Processor Architectures Programming Languages Representations Statistical analysis Training COVID-19 Deep convolutional autoEencoder Hidden representation Kernel density estimation Reconstruction error
ISSN:	0920-8542, 1573-0484
On-line prístup:	Získať plný text
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