Cargando…
Multi-Task Deep Supervision on Attention R2U-Net for Brain Tumor Segmentation
Accurate automatic medical image segmentation technology plays an important role for the diagnosis and treatment of brain tumor. However, simple deep learning models are difficult to locate the tumor area and obtain accurate segmentation boundaries. In order to solve the problems above, we propose a...
Autores principales: | Ma, Shiqiang, Tang, Jijun, Guo, Fei |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8484917/ https://www.ncbi.nlm.nih.gov/pubmed/34604039 http://dx.doi.org/10.3389/fonc.2021.704850 |
Ejemplares similares
-
A Deep Multi-Task Learning Framework for Brain Tumor Segmentation
por: Huang, He, et al.
Publicado: (2021) -
Application of Multi-Scale Fusion Attention U-Net to Segment the Thyroid Gland on Localized Computed Tomography Images for Radiotherapy
por: Wen, Xiaobo, et al.
Publicado: (2022) -
Multi-Scale Squeeze U-SegNet with Multi Global Attention for Brain MRI Segmentation
por: Dayananda, Chaitra, et al.
Publicado: (2021) -
DARU‐Net: A dual attention residual U‐Net for uterine fibroids segmentation on MRI
por: Zhang, Jian, et al.
Publicado: (2023) -
ADU-Net: An Attention Dense U-Net based deep supervised DNN for automated lesion segmentation of COVID-19 from chest CT images
por: Saha, Sanjib, et al.
Publicado: (2023)