Cargando…
MTPA_Unet: Multi-Scale Transformer-Position Attention Retinal Vessel Segmentation Network Joint Transformer and CNN
Retinal vessel segmentation is extremely important for risk prediction and treatment of many major diseases. Therefore, accurate segmentation of blood vessel features from retinal images can help assist physicians in diagnosis and treatment. Convolutional neural networks are good at extracting local...
Autores principales: | Jiang, Yun, Liang, Jing, Cheng, Tongtong, Lin, Xin, Zhang, Yuan, Dong, Jinkun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9229851/ https://www.ncbi.nlm.nih.gov/pubmed/35746372 http://dx.doi.org/10.3390/s22124592 |
Ejemplares similares
-
PCAT-UNet: UNet-like network fused convolution and transformer for retinal vessel segmentation
por: Chen, Danny, et al.
Publicado: (2022) -
TDD-UNet:Transformer with double decoder UNet for COVID-19 lesions segmentation
por: Huang, Xuping, et al.
Publicado: (2022) -
SW-UNet: a U-Net fusing sliding window transformer block with CNN for segmentation of lung nodules
por: Ma, Jiajun, et al.
Publicado: (2023) -
Multiresolution Aggregation Transformer UNet Based on Multiscale Input and Coordinate Attention for Medical Image Segmentation
por: Chen, Shaolong, et al.
Publicado: (2022) -
HUT: Hybrid UNet transformer for brain lesion and tumour segmentation
por: Soh, Wei Kwek, et al.
Publicado: (2023)