Cargando…
TMD-Unet: Triple-Unet with Multi-Scale Input Features and Dense Skip Connection for Medical Image Segmentation
Deep learning is one of the most effective approaches to medical image processing applications. Network models are being studied more and more for medical image segmentation challenges. The encoder–decoder structure is achieving great success, in particular the Unet architecture, which is used as a...
Autores principales: | Tran, Song-Toan, Cheng, Ching-Hwa, Nguyen, Thanh-Tuan, Le, Minh-Hai, Liu, Don-Gey |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7825313/ https://www.ncbi.nlm.nih.gov/pubmed/33419018 http://dx.doi.org/10.3390/healthcare9010054 |
Ejemplares similares
-
A-DenseUNet: Adaptive Densely Connected UNet for Polyp Segmentation in Colonoscopy Images with Atrous Convolution
por: Safarov, Sirojbek, et al.
Publicado: (2021) -
MID-UNet: Multi-input directional UNet for COVID-19 lung infection segmentation from CT images
por: Chi, Jianning, et al.
Publicado: (2022) -
COVID TV-Unet: Segmenting COVID-19 chest CT images using connectivity imposed Unet
por: Saeedizadeh, Narges, et al.
Publicado: (2021) -
TDD-UNet:Transformer with double decoder UNet for COVID-19 lesions segmentation
por: Huang, Xuping, et al.
Publicado: (2022) -
Decoders configurations based on Unet family and feature pyramid network for COVID-19 segmentation on CT images
por: Nguyen, Hai Thanh, et al.
Publicado: (2021)