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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Detalles Bibliográficos
Autores principales: Sarv Ahrabi, Sima, Piazzo, Lorenzo, Momenzadeh, Alireza, Scarpiniti, Michele, Baccarelli, Enzo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8867464/
https://www.ncbi.nlm.nih.gov/pubmed/35228777
http://dx.doi.org/10.1007/s11227-022-04349-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8867464/
https://www.ncbi.nlm.nih.gov/pubmed/35228777
http://dx.doi.org/10.1007/s11227-022-04349-y

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