Cargando…
SA-Net: A scale-attention network for medical image segmentation
Semantic segmentation of medical images provides an important cornerstone for subsequent tasks of image analysis and understanding. With rapid advancements in deep learning methods, conventional U-Net segmentation networks have been applied in many fields. Based on exploratory experiments, features...
Autores principales: | Hu, Jingfei, Wang, Hua, Wang, Jie, Wang, Yunqi, He, Fang, Zhang, Jicong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8046243/ https://www.ncbi.nlm.nih.gov/pubmed/33852577 http://dx.doi.org/10.1371/journal.pone.0247388 |
Ejemplares similares
-
MEA-Net: multilayer edge attention network for medical image segmentation
por: Liu, Huilin, et al.
Publicado: (2022) -
WRANet: wavelet integrated residual attention U-Net network for medical image segmentation
por: Zhao, Yawu, et al.
Publicado: (2023) -
MCA-UNet: multi-scale cross co-attentional U-Net for automatic medical image segmentation
por: Wang, Haonan, et al.
Publicado: (2023) -
MHA-Net: A Multibranch Hybrid Attention Network for Medical Image Segmentation
por: Zhang, Meifang, et al.
Publicado: (2022) -
U-Net combined with multi-scale attention mechanism for liver segmentation in CT images
por: Wu, Jiawei, et al.
Publicado: (2021)