Learning deep features for dead and living breast cancer cell classification without staining
Abstract Automated cell classification in cancer biology is a challenging topic in computer vision and machine learning research. Breast cancer is the most common malignancy in women that usually involves phenotypically diverse populations of breast cancer cells and an heterogeneous stroma. In recen...
Guardado en:
Autores principales: | Gisela Pattarone, Laura Acion, Marina Simian, Emmanuel Iarussi |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/f0931625d1854faaa1f1cbce56932ac0 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Author Correction: Learning deep features for dead and living breast cancer cell classification without staining
por: Gisela Pattarone, et al.
Publicado: (2021) -
Label-free imaging and classification of live P. falciparum enables high performance parasitemia quantification without fixation or staining.
por: Paul Lebel, et al.
Publicado: (2021) -
Deep learning-based transformation of H&E stained tissues into special stains
por: Kevin de Haan, et al.
Publicado: (2021) -
While the dead labour for the living
por: Ida Hillerup Hansen
Publicado: (2019) -
A blue fluorescent labeling technique utilizing micro- and nanoparticles for tracking in LIVE/DEAD® stained pathogenic biofilms of Staphylococcus aureus and Burkholderia cepacia
por: Klinger-Strobel M, et al.
Publicado: (2016)