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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Bibliographic Details
Published in:The Journal of supercomputing Vol. 78; no. 9; pp. 12024 - 12045
Main Authors: Sarv Ahrabi, Sima, Piazzo, Lorenzo, Momenzadeh, Alireza, Scarpiniti, Michele, Baccarelli, Enzo
Format: Journal Article
Language:English
Published: New York Springer US 01.06.2022
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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. Once the model is trained, the encoder can generate the compact hidden representation (the hidden feature vectors) of the training data set. Afterwards, we exploit the obtained hidden representation to build up the target probability density function (PDF) of the training data set by means of kernel density estimation (KDE). Subsequently, in the test phase, we feed a test CT into the trained encoder to produce the corresponding hidden feature vector, and then, we utilise the target PDF to compute the corresponding PDF value of the test image. Finally, this obtained value is compared to a threshold to assign the COVID-19 label or non-COVID-19 to the test image. We numerically check our approach’s performance (i.e. test accuracy and training times) by comparing it with those of some state-of-the-art methods.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-022-04349-y