Cargando…
Twinned Residual Auto-Encoder (TRAE)—A new DL architecture for denoising super-resolution and task-aware feature learning from COVID-19 CT images
The detection of the COronaVIrus Disease 2019 (COVID-19) from Computed Tomography (CT) scans has become a very important task in modern medical diagnosis. Unfortunately, typical resolutions of state-of-the-art CT scans are still not adequate for reliable and accurate automatic detection of COVID-19...
Autores principales: | Baccarelli, Enzo, Scarpiniti, Michele, Momenzadeh, Alireza |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10106117/ https://www.ncbi.nlm.nih.gov/pubmed/37090446 http://dx.doi.org/10.1016/j.eswa.2023.120104 |
Ejemplares similares
-
A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection
por: Scarpiniti, Michele, et al.
Publicado: (2022) -
Exploiting probability density function of deep convolutional autoencoders’ latent space for reliable COVID-19 detection on CT scans
por: Sarv Ahrabi, Sima, et al.
Publicado: (2022) -
How much BiGAN and CycleGAN-learned hidden features are effective for COVID-19 detection from CT images? A comparative study
por: Sarv Ahrabi, Sima, et al.
Publicado: (2022) -
Convolutional auto-encoder for image denoising of ultra-low-dose CT
por: Nishio, Mizuho, et al.
Publicado: (2017) -
Reliable Fault Diagnosis of Bearings Using an Optimized Stacked Variational Denoising Auto-Encoder
por: Yan, Xiaoan, et al.
Publicado: (2021)