Cargando…
Convolutional Blur Attention Network for Cell Nuclei Segmentation
Accurately segmented nuclei are important, not only for cancer classification, but also for predicting treatment effectiveness and other biomedical applications. However, the diversity of cell types, various external factors, and illumination conditions make nucleus segmentation a challenging task....
Autores principales: | Thi Le, Phuong, Pham, Tuan, Hsu, Yi-Chiung, Wang, Jia-Ching |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8878074/ https://www.ncbi.nlm.nih.gov/pubmed/35214488 http://dx.doi.org/10.3390/s22041586 |
Ejemplares similares
-
Anti-Aliasing Attention U-net Model for Skin Lesion Segmentation
por: Le, Phuong Thi, et al.
Publicado: (2023) -
Densely Convolutional Spatial Attention Network for nuclei segmentation of histological images for computational pathology
por: Islam Sumon, Rashadul, et al.
Publicado: (2023) -
Adaptive Attention Convolutional Neural Network for Liver Tumor Segmentation
por: Luan, Shunyao, et al.
Publicado: (2021) -
Improved modeling of human vision by incorporating robustness to blur in convolutional neural networks
por: Jang, Hojin, et al.
Publicado: (2023) -
Cell Nuclei Segmentation in Cytological Images Using Convolutional Neural Network and Seeded Watershed Algorithm
por: Kowal, Marek, et al.
Publicado: (2019)